Logistic Regression

Logistic Regression

  • Logistic Regression is used to perform binary classification, predicting whether a data sample belongs to a positive (present) class, labeled 1 and the negative (absent) class, labeled 0.


  • The Sigmoid Function bounds the product of feature values and their coefficients, known as the log-odds, to the range [0,1], providing the probability of a sample belonging to the positive class.

  • A loss function measures how well a machine learning model makes predictions. The loss function of Logistic Regression is log-loss.

  • A Classification Threshold is used to determine the probabilistic cutoff for where a data sample is classified as belonging to a positive or negative class. The standard cutoff for Logistic Regression is 0.5, but the threshold can be higher or lower depending on the nature of the data and the situation.

  • Scikit-learn has a Logistic Regression implementation that allows you to fit a model to your data, find the feature coefficients, and make predictions on new data samples.

  • The coefficients determined by a Logistic Regression model can be used to interpret the relative importance of each feature in predicting the class of a data sample.

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