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# scikit-learn Cheatsheet

scikit-learn is a popular open-source machine learning library for Python. It provides a wide range of tools for building and evaluating machine learning models, including classification, regression, clustering, and more. This cheatsheet provides a quick reference for some of scikit-learn's unique features, including code blocks for loading data, preprocessing, model selection, and more. Additionally, it includes a list of resources for further learning.

## Loading Data

from sklearn.datasets import load_digits

Load the digits dataset

digits = load_digits()

Split the data into training and testing sets

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.25, random_state=42)

## Preprocessing

Scale the data to have zero mean and unit variance

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()

X_train_scaled = scaler.fit_transform(X_train)

X_test_scaled = scaler.transform(X_test)

Encode categorical variables as integers

from sklearn.preprocessing import LabelEncoder

encoder = LabelEncoder()

y_train_encoded = encoder.fit_transform(y_train)

y_test_encoded = encoder.transform(y_test)

## Model Selection

Train a support vector machine classifier

from sklearn.svm import SVC

clf = SVC(kernel='linear', C=1.0, random_state=42)

clf.fit(X_train_scaled, y_train_encoded)

Evaluate the classifier

from sklearn.metrics import accuracy_score

y_pred = clf.predict(X_test_scaled)

accuracy_score(y_test_encoded, y_pred)

## Cross-Validation

Perform k-fold cross-validation

from sklearn.model_selection import cross_val_score

scores = cross_val_score(clf, X_train_scaled, y_train_encoded, cv=5)

## Grid Search

Perform a grid search over hyperparameters

from sklearn.model_selection import GridSearchCV

param_grid = {'C': [0.1, 1.0, 10.0], 'kernel': ['linear', 'rbf']}

grid_search = GridSearchCV(clf, param_grid, cv=5)

grid_search.fit(X_train_scaled, y_train_encoded)

## Other Useful Features

Train a decision tree classifier

from sklearn.tree import DecisionTreeClassifier

clf = DecisionTreeClassifier(max_depth=3, random_state=42)

clf.fit(X_train_scaled, y_train_encoded)

Visualize the decision tree

from sklearn.tree import plot_tree

plot_tree(clf)

Save and load a model

import joblib

joblib.dump(clf, 'model.joblib')

clf = joblib.load('model.joblib')

## Resources

- [scikit-learn documentation](https://scikit-learn.org/stable/documentation.html)
- [scikit-learn tutorials](https://scikit-learn.org/stable/tutorial/index.html)
- [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/index.html)