For each type of linear regression, it seeks to plot a line of best fit, which is calculated through the method of least squares. As the number of independent variables increases, it is referred to as multiple linear regression. When there is only one independent variable and one dependent variable, it is known as simple linear regression. Linear regression: Linear regression is used to identify the relationship between a dependent variable and one or more independent variables and is typically leveraged to make predictions about future outcomes.This technique is primarily used in text classification, spam identification, and recommendation systems. There are three types of Naïve Bayes classifiers: Multinomial Naïve Bayes, Bernoulli Naïve Bayes, and Gaussian Naïve Bayes. This means that the presence of one feature does not impact the presence of another in the probability of a given outcome, and each predictor has an equal effect on that result. Naive bayes: Naive Bayes is classification approach that adopts the principle of class conditional independence from the Bayes Theorem.When the cost function is at or near zero, we can be confident in the model’s accuracy to yield the correct answer. Neural networks learn this mapping function through supervised learning, adjusting based on the loss function through the process of gradient descent. If that output value exceeds a given threshold, it “fires” or activates the node, passing data to the next layer in the network. Each node is made up of inputs, weights, a bias (or threshold), and an output. Neural networks: Primarily leveraged for deep learning algorithms, neural networks process training data by mimicking the interconnectivity of the human brain through layers of nodes.Below are brief explanations of some of the most commonly used learning methods, typically calculated through use of programs like R or Python: Various algorithms and computations techniques are used in supervised machine learning processes. Linear regression, logistical regression, and polynomial regression are popular regression algorithms. It is commonly used to make projections, such as for sales revenue for a given business. Regression is used to understand the relationship between dependent and independent variables.Common classification algorithms are linear classifiers, support vector machines (SVM), decision trees, k-nearest neighbor, and random forest, which are described in more detail below. It recognizes specific entities within the dataset and attempts to draw some conclusions on how those entities should be labeled or defined. Classification uses an algorithm to accurately assign test data into specific categories.Supervised learning can be separated into two types of problems when data mining-classification and regression: The algorithm measures its accuracy through the loss function, adjusting until the error has been sufficiently minimized. This training dataset includes inputs and correct outputs, which allow the model to learn over time. Supervised learning uses a training set to teach models to yield the desired output.

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