Programming FlashCards - Learn Coding Concepts

Regularization

Regularization techniques like L1 (Lasso) and L2 (Ridge) are used in Logistic Regression to prevent overfitting by penalizing large coefficients. This helps improve model generalization on unseen data by controlling the complexity of the model.

Sklearn

Logistic Regression

Logistic Regression is a statistical method for predicting binary classes. The output is a probability that the given input point belongs to a certain class, making it useful for classification tasks.

Sklearn

Confusion Matrix

A confusion matrix is a table used to evaluate the performance of a classification model. It compares the actual target values with those predicted by the model, providing insights into true positives, false positives, true negatives, and false negatives.

Sklearn

Mean Absolute Error

Mean Absolute Error (MAE) is a metric used to measure the average magnitude of errors in a set of predictions, without considering their direction. It’s the average over the test sample of the absolute differences between prediction and actual observation. Lower MAE indicates better model performance.

Sklearn

CCA

Canonical Correlation Analysis (CCA) is a method used to understand the relationship between two multivariate sets of variables. Sklearn's CCA implementation helps in finding linear combinations of two datasets that are maximally correlated.

Sklearn

PLS Regression

Partial Least Squares (PLS) Regression is a method that finds the fundamental relations between two matrices. It is particularly useful when the predictors are many and highly collinear. PLS is implemented in Sklearn for regression tasks.

Sklearn

Locally Linear Embedding

LLE is a nonlinear dimensionality reduction technique that preserves local neighborhood relationships, revealing intrinsic data structure by reconstructing each point from its neighbors.

Sklearn

t-SNE Dimensionality Reduction

t-SNE (t-Distributed Stochastic Neighbor Embedding) is a powerful manifold learning technique for visualizing high-dimensional data in a low-dimensional space while preserving local data structures.