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## Classification Problem
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In the previous week, we discuss one of the predictive learning tasks, [regression](DDE-1/Regression), we the goal is to estimate the target value from the input array X by using alternative mathematical models. The objective in classification is similar; we use the input feature matrix X to predict a class, a set of discrete values. Herein, the mathematical model divides the feature space (X) into regions, which are separated by the decision boundaries. The learning procedure is, therefore, focused on identifying these hyperplanes.
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### Linear Classifiers: Logistic regression
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The simplest case is where the input data can be separated by using "linear decision planes",i.e., linearly separable. Here linear means that the decision surfaces are linear functions of the input array X. |
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