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Logistic Regression tutorial

Learn Logistic Regression on Skill Zuno. This Logistic Regression tutorial covers New, Easy, Medium, Hard, and Diff levels so you can study practical Logistic Regression problems on skillzunos.com.

Also see the Logistic Regression interview questions.

Logistic regression is a linear model for classification. It passes a weighted sum of the features through the sigmoid function to predict the probability of the positive class, and it is trained by minimising log loss (cross-entropy). Interviews test the maths behind it, how to read its coefficients, regularisation, and how to evaluate a classifier.

Logistic Regression topics covered

• The sigmoid function and log-odds
• Log loss and maximum likelihood estimation
• Training with gradient descent
• Reading coefficients as odds ratios
• L1 and L2 regularisation
• Decision thresholds, precision, recall and ROC AUC
• Multiclass: one-vs-rest and softmax
• LogisticRegression in scikit-learn

What you can learn

• Logistic Regression tutorial for beginners and experienced developers
• Level-based practice: New, Easy, Medium, Hard, and Diff
• Python ML topics alongside Logistic Regression on skillzunos.com

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