From Logistic Regression coefficients to predictions using sigmoid

Before the next exercise, let’s take a look at how the coefficients for logistic regression are used to calculate predicted probabilities, and ultimately make predictions for the class of the response variable.

Recall that logistic regression predicts the probability of class membership, according to the sigmoid equation. In the case of two features with an intercept, the equation is as follows:

p = \frac{1}{1+e^{-(θ_0 + θ_1X_1 + θ_2X_2)}}

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Introduction

Data Exploration and Cleaning

(Challenge) Exploring Remaining Financial Features in Dataset

Introduction to scikit-learn and Model Evaluation

(Challenge) Logistic Regression and Precision-Recall Curve

Details of Logistic Regression and Feature Extraction

(Challenge) Logistic Regression Model and Coefficients

The Bias-Variance Trade-Off

(Challenge) Cross-Validation and Feature Engineering

Decision Trees and Random Forests

(Challenge) Cross-Validation Grid Search with Random Forest

Gradient Boosting, XGBoost, and SHAP Values

(Challenge) XGBoost and SHAP Explanation for Case Study Data

Test Set Analysis, Financial Insights, and Delivery to the Client

(Challenge) Deriving Financial Insights

Appendix

Logistic Regression Predictions Using Sigmoid

From Logistic Regression coefficients to predictions using sigmoid