scikit-learn

An open-source Python library, built on NumPyNumPy, prop. n.: The basic free Python library for working quickly with large grids of numbers; nearly every data-science and machine-learning tool in Python is built on it or copies its style. and SciPy, that gathers the standard toolkit of classical machine learningmachine learning, n.: A way of building software that learns from examples instead of following rules written by a programmer. Shown thousands of labeled photos, for instance, a program can learn to recognize cats.: preprocessing and dimensionality reduction; nearest neighbors, linearlinear regression, n.: Fitting a straight line (or flat surface) through data points to predict one number from others — say, a house's price from its floor area — choosing the line that keeps prediction errors smallest. and logisticlogistic regression, n.: A simple, widely used method for yes-or-no predictions that outputs a probability — say, a 70% chance a customer cancels — by passing a weighted sum of inputs through an S-shaped curve. models, decision treesdecision tree, n.: A model that makes a prediction by asking a series of yes-or-no questions about the input, like a flowchart, with each answer leading to the next question until it reaches a final answer., random forestsrandom forest, n.: A prediction method that builds hundreds of decision trees, each from a random sample of the data and features, and lets them vote. The crowd is far more reliable than any single tree., gradient boostinggradient boosting, n.: A method that builds a strong predictor by adding small decision trees one at a time, each trained to correct the errors that remain after all the trees before it. and support vector machinessupport vector machine, n.: A classic method that separates two groups of examples with the widest possible gap — like laying the broadest possible road between them. A mathematical shortcut lets it draw curved boundaries too.; k-meansk-means clustering, n.: A simple way to split data into a chosen number of groups: guess group centers, assign every point to its nearest center, move each center to the middle of its points, and repeat. and other clustering methods; and tools for cross-validationcross-validation, n.: A fair way to test a model when data are limited: split the data into several parts, train on all but one, test on the part left out, and rotate so every part takes a turn., hyperparameter search and evaluation. Its lasting influence is a uniform estimator interface: every model is an object configured by constructor arguments, trained with fit and applied with predict or transform, so models can be swapped, chained into Pipeline objects and tuned with the same code. It deliberately concentrates on in-memory tabular data and leaves out deep learning and GPU computation. Its conventions became a template for other libraries, including XGBoostXGBoost, prop. n.: A fast, free software tool that makes predictions by adding up hundreds of small decision trees, each one fixing the mistakes of those before it. For years it was behind many winning entries in competitions on spreadsheet-style data.'s and LightGBM's Python interfaces, and it remains a standard tool for teaching and baselines.

Start with a scikit-learn pipeline — scaler plus logistic regression — before reaching for anything deep.