Search

Github

Linkedin

Coding Activities

Scikit-Learn Modules
📎

Scikit-Learn Modules

FUNCTION
Name
Status
Multi-select
URL

sklearn.utils

Internal utilities and helper functions.
Not started

sklearn.tree:

Decision tree models for classification and regression.
Not started

sklearn.tree:

Decision tree models for classification and regression.
Not started

sklearn.svm:

Support Vector Machines for classification and regression
Not started

sklearn.semi_supervised:

Learning with labeled and unlabeled data.
Not started

sklearn.random_projection:

Random projections for dimensionality reduction.
Not started

sklearn.preprocessing:

📂
Preprocessing tools like scaling, normalization, and encoding.
In progress

sklearn.pipeline:

Tools for creating machine learning pipelines.
Not started

sklearn.neural_network:

Multi-layer perceptron (MLP) for classification and regression.
Not started

sklearn.neighbors

Nearest neighbors methods and KNN.
Not started

sklearn.naive_bayes:

Naive Bayes classification algorithms.
Not started

sklearn.multioutput:

Multi-output estimators for regression and classification.
Not started

sklearn.model_selection:

Tools for cross-validation, splitting, and hyper-parameter tuning.
Not started
study

sklearn.metrics:

Metrics for evaluating models.
In progress

sklearn.manifold:

Nonlinear dimensionality reduction techniques (e.g., t-SNE).
Not started

sklearn.linear_model:

Linear regression, logistic regression, and related models.
Not started

sklearn.kernel_ridge:

Kernel ridge regression.
Not started

sklearn.kernel_approximation:

Approximation of kernel functions.
Not started

sklearn.isotonic:

Isotonic regression.
Not started

sklearn.inspection:

Model inspection tools like partial_dependence.
Not started

sklearn.impute

📂
Imputation of missing values.
In progress

sklearn.gaussian_process

Gaussian process regression and classification.
Not started

sklearn.feature_selection

Tools for selecting features based on importance.
Not started

sklearn.feature_extraction

Feature extraction from text and images.
Not started

sklearn.externals

External libraries and utilities (e.g., joblib for saving models).
Not started

sklearn.experimental

Experimental features (subject to change in future releases).
Not started

sklearn.exceptions

Error handling.
Not started

sklearn.ensemble

Ensemble methods like Random Forest, Gradient Boosting, etc.
Not started

sklearn.dummy

Simple baseline estimators.
Not started

sklearn.discriminant_analysis

Linear and Quadratic Discriminant Analysis.
Not started

sklearn.decomposition

Dimensionality reduction techniques (PCA, NMF, etc.).
Not started

sklearn.datasets

Preloaded datasets and dataset loaders.
Not started

sklearn.cross_decomposition

Partial least squares and Canonical Correlation Analysis.
Not started

sklearn.covariance

Covariance estimation and anomaly detection.
Not started

sklearn.compose

Tools for composing pipelines and transformers.
Not started

sklearn.cluster

Clustering algorithms.
Not started

sklearn.calibration

Tools for probability calibration.
Not started

sklearn.base

Base classes and mixins for building custom estimators.
Not started