scikit-learn 1 cheatsheet
scikit-learn 1: Data and splitting
4 common scikit-learn 1 tasks.
Install scikit-learn
Install scikit-learn, which needs NumPy and SciPy (pip installs them for you).
pip install scikit-learn
Load a built-in dataset
Load a small practice dataset as a feature matrix X and target vector y.
from sklearn.datasets import load_iris
X, y = load_iris(return_X_y=True)
Generate a synthetic dataset
Create a random classification problem, good for testing code quickly.
from sklearn.datasets import make_classification
X, y = make_classification(n_samples=500, n_features=8, n_informative=4, random_state=42)
Split into train and test sets
Hold out 20% of the data for testing. stratify keeps class proportions, and random_state makes it repeatable.
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)