Sas jmp customer churn excercise answers
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Hence, in this case, LDA (Linear Discriminant Analysis) is used which reduces the 2D graph into a 1D graph in order to maximize the separability between the two classes. As shown in the given 2D graph, when the data points are plotted on the 2D plane, there’s no straight line that can separate the two classes of the data points completely. Suppose we have two sets of data points belonging to two different classes that we want to classify. So, we will keep on increasing the number of features for proper classification. Using only a single feature to classify them may result in some overlapping as shown in the below figure. It is used to project the features in higher dimension space into a lower dimension space.įor example, we have two classes and we need to separate them efficiently. It is used for modelling differences in groups i.e. Linear Discriminant Analysis or Normal Discriminant Analysis or Discriminant Function Analysis is a dimensionality reduction technique that is commonly used for supervised classification problems. Removing stop words with NLTK in Python.Difference between Batch Gradient Descent and Stochastic Gradient Descent.Difference between Gradient descent and Normal equation.ML | Normal Equation in Linear Regression.Mathematical explanation for Linear Regression working.Linear Regression (Python Implementation).ML | Types of Learning – Supervised Learning.Analysis of test data using K-Means Clustering in Python.Different Types of Clustering Algorithm.Principal Component Analysis with Python.ISRO CS Syllabus for Scientist/Engineer Exam.ISRO CS Original Papers and Official Keys.GATE CS Original Papers and Official Keys.