Machine Learning Algorithm — Logistic Regression

  • we take linear combination (or weighted sum of the input features)
  • we apply the sigmoid function to the result to obtain a number between 0 and 1
  • this number represents the probability of the input being classified as “Yes”
  • instead of RMSE, the cross entropy loss function is used to evaluate the results
  1. Training set — used to train the model, i.e., compute the loss and adjust the model’s weights using an optimization technique.
  2. Validation set — used to evaluate the model during training, tune model hyperparameters (optimization technique, regularization etc.), and pick the best version of the model. Picking a good validation set is essential for training models that generalize well.
  3. Test set — used to compare different models or approaches and report the model’s final accuracy. For many datasets, test sets are provided separately. The test set should reflect the kind of data the model will encounter in the real-world, as closely as feasible.

Model Evaluation Metrics

Model Improvements

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