API Reference

This section provides detailed API documentation for all classes and functions in lostml.

Linear Models

• Linear Regression Models
  • LinearRegression
  • Ridge Regression
  • Lasso Regression
  • Elastic Net
  • Common Parameters
• Logistic Regression
  • LogisticRegression Classifier
  • Parameters
  • Methods
  • Examples
  • How It Works
  • Tips

Neighbors

• K-Nearest Neighbors
  • KNN Classifier
  • Examples

Tree-Based Models

• Decision Tree
  • DecisionTree Class
  • Parameters
  • Examples
  • How It Works
  • Tips
• Random Forest
  • RandomForest Class
  • Parameters
  • Examples
  • How It Works
  • Key Concepts
  • Advantages
  • Tips
  • Comparison with Decision Tree

Base Classes

Utilities

Distance Metrics

lostml.utils.distances.euclidean_distance(x1, x2)

Compute Euclidean distance between points or arrays.

Parameters:

• x1 (array-like) – First point(s). Shape: (n_features,) or (n_samples, n_features)

• x2 (array-like) – Second point(s). Shape: (n_features,) or (n_samples, n_features)

Returns:

Euclidean distance(s). If both inputs are 1D, returns scalar. If inputs are 2D, returns array of distances.

Return type:

float or ndarray

lostml.utils.distances.manhattan_distance(x1, x2)

Compute Manhattan (L1) distance between points or arrays.

Parameters:

• x1 (array-like) – First point(s)

• x2 (array-like) – Second point(s)

Returns:

Manhattan distance(s)

Return type:

float or ndarray