Ridge Regression

In layman’s terms, Ridge regression adds one more term to linear regression’s cost function to reduce error. Ridge regression is a model-tuning method used to analyze data that suffer from multicollinearity. This method performs L2 regularization. When the issue of multicollinearity occurs, least-squares are unbiased, and variances are large; this results in predicted values being…

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Logistic Regression from Scratch

Lets us try to implement logistic regression from scratch in python. Recommended to be read after the Neural Networks release. Importing necessary libraries The dataset we will be using is Pima-Indians-diabetes-database Whose objective is to predict whether or not a patient has diabetes diagnostically. y = data.Outcome.values x_data = data.drop([“Outcome”],axis=1)   After this, the data…

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Logistic Regression

Logistic regression is one of the most popular algorithms for classification problems. It is called regression even though it is not a regression algorithm because the underlying technology is similar to Linear Regression. The term “logistic” comes from the statistical model used (logit model). As seen in earlier releases, classification algorithms are used to classify…

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