Logistic Regression Exercise

Programming for Data Science is a subject we’ve designed to explore the various programming components of data science.

Keywords

data science, data analysis, programming, dataidea

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Week 5: Logistic Regression

1. Making Predictions in Logistic Regression

• Exercise: Implement a logistic regression model to classify data into two classes. Use a synthetic dataset with two features and evaluate the model’s accuracy using confusion matrix.

2. Logistic Regression and Bad Initialization Values

• Exercise: Train a logistic regression model with various initialization strategies. Analyze how different initial weights affect the convergence and performance of the model.

3. Cross Entropy Loss Function

• Exercise: Implement the cross-entropy loss function from scratch for a logistic regression model. Compare its output with PyTorch’s built-in torch.nn.CrossEntropyLoss function.

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4. Softmax Activation in 1 Dimension

• Exercise: Implement the softmax activation function for a vector of logits in a 1D tensor. Use this function to convert raw model outputs into probabilities and interpret the results.

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