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Gate Data Science and AI
·
Module 9
Model Evaluation and Improvement
1
Classification Model Evaluation Metrics
Evaluate classification models using accuracy, precision, recall, and F1-score.
Evaluating Regression Models with MSE and R-squared
Evaluate regression models using Mean Squared Error (MSE) and R-squared.
3
Bias, Variance, Underfitting, and Overfitting
Describe the concepts of bias, variance, underfitting, and overfitting.
4
Regularization: Lasso and Ridge
Apply L1 (Lasso) and L2 (Ridge) regularization to mitigate overfitting.
5
K-Fold Cross-Validation for Model Generalization
Assess model generalization performance using k-fold cross-validation.
6
Understanding Sampling Distributions with the Central Limit Theorem
Apply the Central Limit Theorem to understand sampling distributions.
7
Hypothesis Testing with T-Tests
Formulate hypotheses and perform one-sample and two-sample t-tests.
8
Confidence Intervals for Population Means
Construct and interpret confidence intervals for population means.
Previous module
Foundations of Machine Learning
Next module
Advanced Machine Learning Models