ppt presentation on supervised learning

Supervised Learning PPT

• By Alex Wilson
• Published May 5, 2019
• Updated May 5, 2019
• 10 mins read

Supervised learning is one of the key branches of machine learning , where the algorithm learns from labeled training data to make predictions or take actions. It involves mapping input data to known output data and enhancing its performance over time through feedback. This article provides an introduction to supervised learning, its benefits, and applications.

Key Takeaways

• Supervised learning is a branch of machine learning where algorithms learn from labeled training data to make predictions or take actions.
• It involves mapping input data to known output data and improving algorithm performance through feedback.
• Supervised learning has various applications in fields like finance, healthcare, and natural language processing.
• It enables automated decision-making, pattern recognition, and predictive modeling.

What is Supervised Learning?

In supervised learning, an algorithm uses a **labeled dataset** to learn patterns and relations between input data features (**independent variables**) and desired outputs (**dependent variables**). The algorithm then applies this learned information to make accurate predictions or take appropriate actions on new, unseen data. *This approach requires human involvement to label the training data, making it different from unsupervised learning.*

Types of Supervised Learning Algorithms

There are several types of supervised learning algorithms, each suited to different problem domains :

• **Classification:** This algorithm classifies input data into distinct categories or classes.
• **Regression:** It predicts continuous numerical values based on input variables.
• **Decision Trees:** These are tree-like models that make decisions by following a set of predefined rules.

*For instance, classification algorithms are commonly used in spam email filtering, while regression algorithms are useful for predicting housing prices.*

Applications of Supervised Learning

Supervised learning has a vast range of applications across various industries:

These are just a few examples of how supervised learning is revolutionizing industries worldwide, by providing valuable insights, automating decision-making, and streamlining processes.

Challenges and Limitations

While supervised learning is a powerful technique, it does come with certain challenges and limitations:

• **Need for labeled data:** Labeled datasets are required for training supervised learning algorithms, making data collection and labeling a time-consuming process.
• **Bias in training data:** If the training data is biased or unrepresentative, it can lead to biased predictions or decisions.
• **Overfitting or underfitting:** Algorithms may be too specific or too general, failing to generalize well on new, unseen data.

*Overcoming these challenges requires careful data preprocessing , selecting appropriate algorithms, and regular model evaluation and improvement.*

Advantages of Supervised Learning

Supervised learning offers several advantages that contribute to its popularity:

• **Accuracy:** Supervised learning algorithms can achieve high prediction accuracy when trained on quality data.
• **Automation:** It enables the automation of decision-making processes, reducing human effort and potential errors.
• **Pattern Recognition:** These algorithms can identify patterns in complex, high-dimensional data that may not be evident to humans.

*This combination of accuracy, automation, and pattern recognition makes supervised learning invaluable in various domains.*

Supervised learning is a fundamental branch of machine learning that allows algorithms to learn patterns and make predictions from labeled training data. With applications in finance, healthcare, and natural language processing, supervised learning is transforming industries by enabling automated decision-making, pattern recognition, and predictive modeling. Despite its challenges and limitations, the accuracy and automation it offers make it a significant tool in the data scientist’s arsenal.

Common Misconceptions

Supervised learning is the only machine learning approach :.

• Supervised learning is just one type of machine learning, and there are other approaches such as unsupervised learning and reinforcement learning.
• Unsupervised learning deals with finding patterns and relationships in data without labeled examples, while reinforcement learning focuses on training algorithms to make decisions based on trial and error.
• Each of these machine learning approaches has its own significance and use cases in solving various problems.

Supervised Learning Requires Large Amounts of Labeled Data :

• While having a large labeled dataset can be beneficial, supervised learning does not always require a massive amount of labeled data.
• Techniques such as transfer learning and active learning can help in training models with limited labeled data.
• Transfer learning allows the model to leverage knowledge from a pre-trained model on a related task, while active learning enables the model to select the most informative samples to label.

Supervised Learning is Fully Automated and Requires No Human Intervention:

• Although supervised learning algorithms can learn patterns and make predictions autonomously, they still require human intervention in various stages.
• Human involvement is necessary for tasks such as data preprocessing, feature selection, algorithm selection, and evaluating model performance.
• Domain expertise also plays a crucial role in interpreting and validating the results obtained from supervised learning models.

Supervised Learning Guarantees Accuracy and Perfect Predictions:

• Supervised learning algorithms strive to make accurate predictions, but they cannot guarantee 100% accuracy in all cases.
• The performance of supervised learning models depends on factors such as data quality, feature engineering, model selection, and the complexity of the problem.
• It is common for models to encounter challenges in dealing with noisy or incomplete data, resulting in lower prediction accuracy.

Supervised Learning Cannot Handle New or Unseen Data :

• Supervised learning models can generalize from the training data and make predictions on unseen data if the patterns learned are representative of the underlying distribution.
• However, if the distribution of the unseen data significantly differs from the training data, the model’s performance may suffer.
• Techniques like cross-validation and regular model retraining can help ensure that the model performs well and adapts to new data.

Supervised Learning Algorithms

Supervised learning is a type of machine learning where the algorithm learns from labeled data to predict or classify new examples. Various supervised learning algorithms are used in different domains to solve a wide range of problems. In this article, we will examine ten popular supervised learning algorithms and their key characteristics.

1. Logistic Regression

Logistic regression is a binary classification algorithm widely used in disciplines such as medical research and social sciences. It estimates the probability of a binary response variable based on a set of predictor variables.

2. Support Vector Machines (SVM)

Support Vector Machines are powerful classifiers used for both classification and regression tasks. They find an optimal hyperplane separating different classes while maximizing the margin between them, enabling effective decision boundaries.

3. Decision Trees

Decision trees create tree-like models of decisions and their possible consequences. By splitting the data based on different attributes, decision trees can classify new instances by traversing these branches.

4. Random Forests

Random Forests aggregate multiple decision trees to create a robust and accurate ensemble model. Combining predictions from various trees reduces overfitting and improves overall performance.

5. Naive Bayes

Naive Bayes classifiers are based on Bayes’ theorem and make strong independence assumptions between features. These classifiers efficiently determine probabilities of different classes and are widely used in text categorization and spam detection.

6. K-Nearest Neighbors (KNN)

K-nearest neighbors is a non-parametric algorithm that classifies instances based on their similarity with nearby data points. By assigning a new instance to the class most common among its k nearest neighbors, KNN provides simple yet effective predictions.

7. Artificial Neural Networks

Artificial Neural Networks (ANNs) are algorithms inspired by the human brain’s structure and functioning. With interconnected nodes or “neurons,” ANNs can learn complex patterns and relationships, making them suitable for various tasks such as image recognition and natural language processing.

8. Gradient Boosting

Gradient Boosting is an ensemble method where weak learners are combined to create a stronger model. By sequentially adding models that correct the previous ones’ mistakes, Gradient Boosting produces highly accurate predictions in fields like ranking and anomaly detection.

9. Linear Regression

Linear regression, a fundamental algorithm in statistics, estimates the relationship between a dependent variable and one or more independent variables. It forms a line that best fits the data, allowing prediction of future outcomes based on observed trends.

10. Ensemble Methods

Ensemble methods combine multiple models to make predictions with improved accuracy and generalization. These techniques include Bagging, where each model performs independent sampling, and Stacking, which combines outputs from different models to create a meta-model.

In conclusion, these ten supervised learning algorithms offer diverse techniques to tackle various data-driven problems. From traditional approaches like logistic regression and linear regression to more complex ones like artificial neural networks and ensemble methods, each algorithm has its strengths and areas of application. Understanding and selecting the right algorithm for a given task can greatly enhance the process of building successful machine learning models.

Frequently Asked Questions

What is supervised learning.

Supervised learning is a machine learning technique where a model is trained using labeled data. The model learns from a set of input-output pairs and then makes predictions or classifications for unseen data based on the patterns it learns.

How does supervised learning work?

In supervised learning, the model is provided with a dataset that contains input features and corresponding labels. It learns by analyzing the features and labels to identify patterns and relationships. The model then uses this learned knowledge to predict or classify future unseen data.

What are the types of supervised learning algorithms?

There are various types of supervised learning algorithms, including:

• Linear regression
• Logistic regression
• Decision trees
• Random forests
• Support vector machines
• Naive Bayes
• Neural networks

What is the difference between classification and regression in supervised learning?

In classification, the target variable is categorical, and the goal is to classify data into predefined classes or categories. Regression, on the other hand, deals with continuous target variables and aims to predict numerical or continuous values.

What is the role of training and testing data in supervised learning?

Training data is used to build and train the model, while testing data is used to assess the performance of the trained model. By evaluating the model on unseen testing data, we can measure how well the model generalizes to new data and make necessary adjustments if required.

What is the process of evaluating a supervised learning model?

Evaluating a supervised learning model involves assessing its performance using various metrics such as accuracy, precision, recall, F1 score, and confusion matrix. These metrics provide insights into how well the model is performing and can help in fine-tuning the model or selecting the best algorithm.

Can supervised learning models overfit the data?

Yes, supervised learning models can overfit the data. Overfitting occurs when a model becomes too complex and fits the training data too closely. As a result, the model may not generalize well to unseen data. Techniques like regularization, cross-validation, and early stopping can help prevent overfitting.

What is the importance of feature selection in supervised learning?

Feature selection plays a crucial role in supervised learning as it helps in identifying the most relevant features that have the strongest impact on the prediction or classification task. Selecting the right set of features can improve the model’s performance, reduce computational complexity, and avoid overfitting.

Is labeled data necessary for supervised learning?

Yes, labeled data is necessary for supervised learning. The presence of labeled data enables the model to learn from known examples and make predictions on unseen data. Without labeled data, the model would not have the necessary information to generalize and make accurate predictions.

What are the applications of supervised learning?

Supervised learning has numerous applications, including:

• Email spam filtering
• Handwriting recognition
• Image classification
• Sentiment analysis
• Speech recognition
• Stock market prediction
• Medical diagnosis

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Supervised Machine Learning Algorithms. Taxonomy of Machine Learning Methods. The main idea of machine learning (ML) To use computers to learn from massive amounts of data For tedious or unstructured data, machines can often make better and more unbiased decisions than a human learner

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Taxonomy of Machine Learning Methods • The main idea of machine learning (ML) • To use computers to learn from massive amounts of data • For tedious or unstructured data, machines can often make better and more unbiased decisions than a human learner • ML forms the core of artificial intelligence (AI) • Especially in the era of big data • Need to write a computer program based on a model algorithm • Learning from given data objects, one can reveal the categorical class or experience affiliation of future data to be tested • Essentially defines ML as an operational term

Taxonomy of Machine Learning Methods (cont.) • To implement an ML task • Need to explore or construct computer algorithms to learn from data • Make predictions on data based on their specific features, similarities, or correlations • ML algorithms are operated by building a decision-making model from sample data inputs • Defines the relationship between features and labels • A feature is an input variable for the algorithm • A label is an output variable for the algorithm • The outputs are data-driven predictions or decisions • One can handle the ML process subjectively • By finding the best fit to solve the decision problem based on the characteristics in data sets

Classification by Learning Paradigms • ML algorithms can be built with different styles in order to model a problem • The style is dictated by the interaction with the data environment • Expressed as the input to the model • The data interaction style decides the learning models that a ML algorithm can produce • The user must understand the roles of the input data and the model’s construction process • The goal is to select the ML model that can solve the problem with the best prediction result • ML sometime overlaps with the goal of data mining

Classification by Learning Paradigms (cont.) • Three classes of ML algorithms based on different learning styles • Supervised, unsupervised, and semi-supervised • Three ML methods are viable in real-life applications • The style is hinged on how training data is used in the learning process

Classification by Learning Paradigms (cont.) • Supervised learning • The input data is called training data with a known label or result • A model is constructed through training by using the training dataset • Improved by receiving feedback predictions • The learning process continues until the model achieves a desired level of accuracy on the training data • Future incoming data without known labels is tested on the model with an acceptable level of accuracy • Unsupervised learning • All input data are not labeled with a known result

Classification by Learning Paradigms (cont.) • A model is generated by exploring the hidden structures present in the input data • To extract general rules, go through a mathematical process to reduce redundancy, or organize data by similarity testing • Semi-supervised learning • The input data is a mixture of labeled and unlabeled examples • The model must learn the structures to organize the data in order to make predictions possible • Under different assumptions on how to model the unlabeled data

Supervised Machine Learning Algorithms • In a supervised ML system • The computer learns from a training data set of {input, output} pairs • The input comes from sample data given in a certain format • e.g., The credit reports of borrowers • The output may be discrete • e.g., yes or no to a loan application • The output can be also continuous • e.g., The probability distribution that the loan can be paid off in a timely manner • The goal is to work out a reliable ML model • Can map or produce the correct outputs from new inputs that were unseen before

Supervised Machine Learning Algorithms (cont.) • Four families of supervised ML algorithms • Regression, decision trees, Bayesian networks, and support vector machines • The ML system acts like a finely tuned predictor function g(x) • The learning system is built with a sophisticated algorithm to optimize this function • e.g., Given an input data x in a credit report of a borrower, the bank will make a loan decision based on the predicted outcome • The learning process is iteratively refined using an error criterion to make better predictions • Minimizes the error between predicted value and actual experience in input data

Supervised Machine Learning Algorithms (cont.) • The iterative trial-and-error process • Suggested for machine learning algorithms to train a model

Regression Analysis • The outputs of regression are continuous rather than discrete • Finds the causal relationship between the input and output variables • Apply mathematical statistics to establish dependent variables and independent variables in learning • The independent variables are the inputs of the regression process, aka the predictors • The dependent variable is the output of the process • Essentially performs a sequence of parametric or nonparametric estimations • Careful to make the predictions • Causality may lead to illusions or false relationships to mislead the users

Regression Analysis (cont.) • The estimation function can be determined • By experience using a priori knowledge or visual observation of the data • The regression method can be applied to classify data by predicting the category tag of data • Regression analysis determines the quantitative relation in a learning process • How the value of the dependent variable changes • When any independent variable varies while the other independent variables are left unchanged • Regression analysis estimates the average value of the dependent variable when the independent variables are fixed

Regression Analysis (cont.) • The estimated value is a function of the independent variables known as the regression function • Can be described by a probability distribution • Most regression methods are parametric naturally • Need to calculate the undetermined coefficients of the function by using some error criteria • With a finite dimension in the analysis space • Nonparametric regression may be infinite-dimensional • Accuracy or performance depends on the quality of the dataset used • Related to the data generation process and the underlying assumptions made

Regression Analysis (cont.) • Regression offers estimation of continuous response variables • As opposed to the discrete decision values used in classification that demand higher accuracy • In the formulation of a regression process • The unknown parameters are often denoted as β • May appear as a scalar or a vector • The independent variables are denoted by a vector X and a dependent variable as Y • When multiple dimensions are involved, these parameters are vectors in form • A regression model establishes the approximated relation between X, β, and Y:

Regression Analysis (cont.) • The function f(X, β) is approximated by the expected value E(Y|X) • The regression function f is based on the knowledge of the relationship between a continuous variable Y and vector X • If no such knowledge is available, an approximated handy form is chosen for f • Measuring the Height after Tossing a Small Ball in the Air • Measure its height of ascent h at the various time instant t • The relationship is modeled as • β1 determines the initial velocity of the ball

Regression Analysis (cont.) • β2 is proportional to standard gravity • ε is due to measurement errors • Linear regression is used to estimate the values of β1 and β2 from the measured data • This model is nonlinear with respect to the time variable t • But it is linear with respect to parameters β1 and β2 • Consider k components in the vector of unknown parameters β • Three models to relate the inputs to the outputs • Depending on the relative magnitude between the number N of observed data points of the form (X, Y) and the dimension k of the sample space

Regression Analysis (cont.) • When N < k, most classical regression analysis methods can be applied • Most classical regression analysis methods can be applied • The defining equation is underdetermined • No enough data to recover the unknown parameters β • When N = k and the function f is linear • The equation Y = f (X, β) can be solved exactly without approximation • There are N equations to solve N components in β • The solution is unique as long as the X components are linearly independent • If f is nonlinear, many solutions may exist or no solution at all

Regression Analysis (cont.) • In general, the situation with N > k data points • Enough information in the data that can estimate a unique value for β under an overdetermined situation • The measurement errors εi follows a normal distribution • There exists an excess of information contained in (N - k) measurements • Known as the degrees of freedom of the regression • Regression with a Necessary Set of Independent Measurements • Need the necessary number of independent data to perform the regression analysis of continuous data measurements

Regression Analysis (cont.) • Consider a regression model with four unknown parameters, 𝛽0, 𝛽1, 𝛽2 and 𝛽3 • An experimenter performs 10 measurements • All at exactly the same value of independent variable vector X = (X1, X2, X3, X4) • Regression analysis fails to give a unique set of estimated values for the four unknown parameters • Not get enough information to perform the prediction • Only can estimate the average value and the standard deviation of the dependent variable Y • Measuring at two different values of X • Only gives enough data for a regression with two unknowns, but not for three or more unknowns • Only if performs measurements at four different values of the independent variable vector X

Regression Analysis (cont.) • Regression analysis will provide a unique set of estimates for the four unknown parameters in β • Basic assumptions on regression analysis under various error conditions • The sample is representative of the data space involved • The error is a random variable with a mean of zero conditioned over the input variables • The independent variables are measured with no error • The predictors are linearly independent • The errors are uncorrelated • The variance of error is a constant across observations

Linear Regression • Regression analysis includes linear regression and nonlinear regression • Unitary linear regression analysis • Only one independent variable and one dependent variable are included in the analysis • The approximate representation for the relation between the two can be conducted with a straight line • Multivariate linear regression analysis • Two or more independent variables are included in regression analysis • Linear relation between dependent variable and independent variables • The model of a linear regression y = f(X)

Linear Regression (cont.) • X = (x1, x2,⋯, xn) with n  1 is a multidimensional vector and y is scalar variable • f(X) is a linear predictor function used to estimate the unknown parameters from data • Linear regression is applied mainly in the two areas • An approximation process for prediction, forecasting, or error reduction • Predictive linear regression models for an observed data set of y and X values • The fitted model makes a prediction of the value of y for future unknown input vector X • To quantify the strength of the relationship between output y and each input component Xj

Linear Regression (cont.) • Assess which Xj is irrelevant to y and which subsets of the Xj contain redundant information about y • Major steps in linear regression

Unitary Linear Regression • Crickets chirp more frequently on hotter days than on cooler days

Unitary Linear Regression (cont.) • Consider a set of data points in a 2D sample space (x1, y1), (x2, y2), ..., (xn, yn) • Mapped into a scatter diagram • If can be covered approximately by a straight line: y = ax + b + ε • x is an input variable, y is an output variable in the real number range, a and b are coefficients • ε is a random error, and follows a normal distribution with mean E(ε)and variance Var(ε) • Need to work out the expectation by using a linear regression expression: y = ax + b • The main task is to conduct estimations for coefficient a and b via observation on n groups of input samples

Unitary Linear Regression (cont.) • Fit linear regression models with a least squares approach • The approximation is shown by a linear line • Amid the middle or center of all data points in the data space • The residual error (loss) of a unitary model

Unitary Linear Regression (cont.) • The convex objective function is given by • To minimize the sum of squares, need to calculate the partial derivative of Q with respect to , and make them zero • are mean value for input variable and dependent variable, respectively

Unitary Linear Regression (cont.) • After working out the specific expression for the model • Need to know the fitting degree to the dataset • If the expression can express the relation between the two variables and can be used in actual predictions • To figure out the estimated value of the dependent variable with • For each sample in the training data set

Unitary Linear Regression (cont.) • The closer the coefficient of determination R2 is to 1, the better the fitting degree is • The further R2 is away from 1, the worse fitting degree is • Linear regression can also be used for classification • Only used in a binary classification problem • Decide between the two classes • For multivariate linear regression, this method is also applied to classify a dataset

Unitary Linear Regression (cont.) • Healthcare Data Analysis • Obesity is reflected by the weight index • More likely to have high blood pressure or diabetes • Predict the relationship between obesity and high blood pressure • The dataset for body weight index and blood pressure of some people at a hospital in Wuhan

Unitary Linear Regression (cont.) • Conduct a preliminary judgment on what is the datum of blood pressure of a person with a body weight index of 24 • A prediction model with two variables • The unitary linear regression may be considered • Determine distribution of the data points • Scatter diagram for body weight index-blood pressure

Unitary Linear Regression (cont.) • All data points are almost on or below the straight line • Being linearly distributed • The data space is modeled by a unitary linear regression process • By the least square method • Get a = 1.32 and b = 96.58 • Therefore we have y = 1.32x + 96.58 • A significance test is needed to verify whether the model will fit well with the current data • A prediction is made through calculation • The mean residual and coefficient of determination of the model are: average error is 1.17 and R2 = 0.90

Unitary Linear Regression (cont.) • The mean residual is much less than the mean value 125.6 of blood pressure • The coefficient of determination is close to 1 • This regression equation is significant • Can fit well into the dataset • Predictions may be conducted for unknown data on this basis • Given body weight index, the value of blood pressure of a person may be determined with the model • Substitute 24 for x • Can get the value of blood pressure of that person as y = 1.32 × 24 + 96.58 = 128

Multiple Linear Regression • During solving actual problems • Often encounter many variables • e.g., The scores of a student may be influenced by factors like earnestness in class, preparation before class and review after class • e.g., The health of a man is not only influenced by the environment, but also related to the dietary habits • The model of unitary linear regression is not adapted to many conditions • Improve it with a model of multivariate linear regression analysis • Consider the case of m input variables • The output is expressed as a linear combination of the input variables

Multiple Linear Regression (cont.) • 𝛽0, 𝛽1,⋯, 𝛽m, 𝜎2 are unknown parameters • ε complies with normal distribution • The mean value is 0 and the variance is equal to 𝜎2 • By working out the expectation for the structure to get the multivariate linear regression equation • Substituted y for E(y) • Its matrix form is given as E(y) = X𝛽 • X = [1, x1,⋯, xm], 𝛽 = [𝛽0, 𝛽1,⋯, 𝛽m]T • Our goal is to compute the coefficients by minimizing the objective function

Multiple Linear Regression (cont.) • Defined over n sample data points • To minimize Q, need to make the partial derivative of Q with respect to each βi zero • The multiple linear regression equation

Multiple Linear Regression (cont.) • Multivariate regression is an expansion and extension of unitary regression • Identical in nature • The range of applications is different • Unitary regression has limited applications • Multivariate regression is applicable to many real-life problems • Estimate the Density of Pollutant Nitric Oxide in a Spotted Location • Estimation of the density of nitric oxide (NO) gas, an air pollutant, in an urban location • Vehicles discharge NO gas during their movement

Multiple Linear Regression (cont.) • Creates a pollution problem proven harmful to human health • The NO density is attributed to four input variables • Vehicle traffic, temperature, air humidity, and wind velocity • 16 data points collected in various observed spotted locations in the city • Apply the multiple linear regression method to estimate the NO density • In testing a spotted location measured with a data vector of {1436, 28.0, 68, 2.00} for four features {x1, x2, x3, x4}, respectively • X = [1, xn1, xn2, xn3, xn4]T and the weight vector W = [b, β1, β2, β3, β4]T for n = 1,2,.…,16

Multiple Linear Regression (cont.)

Multiple Linear Regression (cont.) • e.g., for the first row of training data, [1300, 20, 80, 0.45, 0.066], X1 = [1, 1300, 20, 80, 0.45]T, which gives the output value y1 = 0.066 • Need to compute W = [b, β1, β2, β3, β4]T and minimize the mean square error • The 16 × 5 matrix directly obtained from the sample data table • y = [0.066, 0.005,.…, 0.039]Tis the given column vector of data labels

Multiple Linear Regression (cont.) • To make the prediction results on the testing sample vector x = [1, 1300, 20, 80, 0.45]T • By substituting the weight vector obtained • The final answer is {β1 = 0.029, β2 = 0.015, β3 = 0.002, β4 = −0.029, b = 0.070} • The NO gas density is predicted as = 0.065 or 6.5%

Logistic Regression Method • Many problems require a probability estimate as output • Logistic regression is an extremely efficient mechanism for calculating probabilities • Commonly used in fields like data mining, automatic diagnosis for diseases, and economic predictions • The logistic model may be used to solve problems of binary classification • In solving a classification problem • The inputs are divided into two or more classes • The learner must produce a model that assigns unseen inputs to one or more of these classes • Typically tackled in a supervised way

Logistic Regression Method • Spam filtering is a good example of classification • The inputs are e-mails, blogs, or document files • The output classes are spam and non-spam • For logistic regression classification • The principle is to conduct classification to sample data with a logistic function • Maps logistic regression output to probabilities • Known as a sigmoid function • The input domain of the sigmoid function is (-∞, +∞) and the range is (0, 1) • Can regard the sigmoid function as a probability density function for sample data

Logistic Regression Method (cont.) • The function image is sensitive, if z = 0 • And not sensitive if z ≫ 0 or z ≪ 0 z

Logistic Regression Method (cont.) • The basic idea for logistic regression • Sample data may be concentrated at both ends of the by the use of intermediate feature z of the sample • Can be divided into two classes • Consider vector X = (x1,⋯, xm) with m independent input variables • Each dimension of X stands for one attribute (feature) of the sample data (training data) • Multiple features of the sample data are combined into one feature by • Figure out the probability of the z feature with designated data

Logistic Regression Method (cont.) • And apply the sigmoid function to act on that feature • Obtain the expression for the logistic regression • During combining of multiple features into one feature

Logistic Regression Method (cont.) • Make use of the linear function • The coefficient of the linear function, i.e., feature weight of sample data, needs to be determined • Maximum likelihood Estimation (MLE) is adopted to transform it into an optimization problem • Attempts to find the parameter values that maximize the likelihood function, given the observations • The coefficient is determined through the optimization method • The loss function is Log Loss • D is the data set containing many labeled examples, i.e., (x, y) pairs

Logistic Regression Method (cont.) • y is the label in a labeled example and its value must either be 0 or 1 • y′ is the predicted value, somewhere between 0 and 1, given the set of features in x • Minimizing this negative logarithm of the likelihood function yields a maximum likelihood estimate • Logistic regression returns a probability • To map a regression value to a binary category must define a classification or decision threshold • Thresholds are problem-dependent • Tempting to assume that it should always be 0.5 • Its value must be tuned • Part of choosing a threshold is assessing how much one will suffer for making a mistake

Logistic Regression Method (cont.) • General steps for logistic regression • Accuracy is one metric for evaluating classification models • The fraction of predictions the model gets right

Logistic Regression Method (cont.) • Four possible statuses for binary classification • TP (True Positive) refers to an outcome where the model correctly predicts the positive class • TN (True Negative) means an outcome where the model correctly predicts the negative class • FP (False Positive) is an outcome where the model incorrectly predicts the positive class • FN (False Negative) an outcome where the model incorrectly predicts the negative class

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Supervised Learning

Supervised Learning. Introduction. Key idea Known target concept (predict certain attribute) Find out how other attributes can be used Algorithms Rudimentary Rules (e.g., 1R) Statistical Modeling (e.g., Na ï ve Bayes) Divide and Conquer: Decision Trees Instance-Based Learning

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Algorithms for Distributed Supervised and Unsupervised Learning

Algorithms for Distributed Supervised and Unsupervised Learning. Haimonti Dutta The Center for Computational Learning Systems (CCLS) Columbia University, New York. About ME. BCSE from Jadavpur University, Kolkata MS from Temple University, PA

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Supervised learning

Supervised learning. Early learning algorithms First order gradient methods Second order gradient methods. Early learning algorithms. Designed for single layer neural networks Generally more limited in their applicability Some of them are Perceptron learning LMS or Widrow- Hoff learning

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Supervised Learning. The supervisor: Perrin Westrich. Outline. What is supervised learning? What is it used for? Approaches. What is supervised learning?. A machine learning technique Training data Validation. Approaches. Neural networks Decision trees Inductive Logic Programming.

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Supervised machine learning

Supervised machine learning. 01/24/2012. Agenda. 0. Introduction of machine learning --Some clinical examples Introduction of classification 1. Cross validation 2. Over-fitting Feature (gene) selection Performance assessment Case study (Leukemia)

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Introduction to Supervised Machine Learning Concepts

Introduction to Supervised Machine Learning Concepts. PRESENTED BY B. Barla Cambazoglu ⎪ February 21, 2014. Guest Lecturer’s Background. Lecture Outline. Basic concepts in supervised machine learning Use case: Sentiment-focused web crawling. Basic Concepts. What is Machine Learning?.

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Supervised Learning. Introduction. Why “Learn”?. Machine learning is programming computers to optimize a performance criterion using example data or past experience. There is no need to “learn” to calculate payroll Learning is used when: Human expertise does not exist (navigating on Mars),

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Business School Institute of Business Informatics. Supervised Learning. Uwe Lämmel. www.wi.hs-wismar.de/~laemmel [email protected]. Neural Networks. Idea Artificial Neuron &amp; Network Supervised Learning Unsupervised Learning Data Mining – other Techniques. Feed-Forward Networks

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Machine Learning Evolutionary Algorithms

Machine Learning Evolutionary Algorithms. You are here. What is Evolutionary Computation?. An abstraction from the theory of biological evolution that is used to create optimization procedures or methodologies, usually implemented on computers, that are used to solve problems.

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Machine Learning Algorithms

Machine Learning Algorithms. and the BioMaLL library. CBB 231 / COMPSCI 261. B. Majoros. Bioinformatics Machine Learning Library. Part I. Overview. Current Contents. Classification methods K-Nearest Neighbors w/Mahalanobis Distance Naive Bayes Linear Discriminant Analysis

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Evaluation of Supervised Learning Algorithms on Gene Expression Data CSCI 6505 – Machine Learning

Machine Learning. Prediction. Evaluation of Supervised Learning Algorithms on Gene Expression Data CSCI 6505 – Machine Learning. Adan Cosgaya [email protected] Winter 2006 Dalhousie University. Outline. Introduction Definition of the Problem Related Work Algorithms

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Supervised Learning. Berlin Chen 2005. References: 1. E. Alpaydin, Introduction to Machine Learning , Chapter 2 2. Tom M. Mitchell, Machine Learning , Chapter 7 3. T. Hastie et al., The Elements of Statistical Learning , Chapter 2. Supervised Learning. Learning with a Teacher

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Supervised Learning. Berlin Chen Graduate Institute of Computer Science &amp; Information Engineering National Taiwan Normal University. References: 1. E. Alpaydin, Introduction to Machine Learning , Chapter 2 2. Tom M. Mitchell, Machine Learning , Chapter 7

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Unsupervised Machine Learning Algorithms

Unsupervised Machine Learning Algorithms. Unsupervised Machine Learning Algorithms. Unsupervised learning is typically used in finding special relationships within data set No training examples used in this process The system is given a set of data to find the patterns and correlations therein

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Introduction to Machine Learning Algorithms

Introduction to Machine Learning Algorithms. What is Artificial Intelligence (AI)?. Design and study of computer programs that behave intelligently . Designing computer programs to make computers smarter .

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Optimization Algorithms for Machine Learning

Optimization is the most crucial part of machine learning algorithms. It begins with defining loss function/cost function and ends with minimizing loss and cost using optimization algorithms These help us maximize or minimize an error function. The internal parameters of a model play a very important role in efficiently and effectively training a model and producing accurate results.

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Supervised Learning Types, Algorithms, and Applications

https://nixustechnologies.com/supervised-machine-learning/

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