🤖 Scikit-learn

What is scikit-learn?

scikit-learn (sklearn) is Python’s core general-purpose machine learning library — consistent APIs for preprocessing, supervised/unsupervised models, model selection, and evaluation, all built on top of NumPy and interoperable with pandas DataFrames.

import sklearn
from sklearn.model_selection import train_test_split

Version note

Assumes scikit-learn ≥ 1.3. Key modern conventions: set_output(transform="pandas") for DataFrame-native pipelines, and consistent get_feature_names_out() across transformers.

📂 Folder Contents

🗺️ Conceptual Map

graph TD
    A[scikit-learn] --> B[Preprocessing]
    A --> C[Model Selection]
    A --> D[Supervised Learning]
    A --> E[Unsupervised Learning]
    A --> F[Evaluation]

    B --> B1[Scalers / Encoders]
    B --> B2[Imputers]
    B --> B3[Pipelines]

    C --> C1[train_test_split]
    C --> C2[Cross-Validation]
    C --> C3[Hyperparameter Tuning]

    D --> D1[Linear Models]
    D --> D2[Tree / Ensemble]
    D --> D3[SVM]
    D --> D4[KNN / Naive Bayes]
    D --> D5[Neural Nets]

    E --> E1[Clustering]
    E --> E2[Dimensionality Reduction]

    F --> F1[Classification Metrics]
    F --> F2[Regression Metrics]

⚡ Quick Reference — Typical Workflow

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report
 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
 
pipe = Pipeline([
    ("scaler", StandardScaler()),
    ("model", LogisticRegression())
])
 
pipe.fit(X_train, y_train)
y_pred = pipe.predict(X_test)
 
print(accuracy_score(y_test, y_pred))
print(classification_report(y_test, y_pred))

🔗 Related in LORE

• Pandas Reference — data loading/cleaning before feeding into sklearn
• NumPy Reference — sklearn arrays, math under the hood
• ML Study Notes — Cost Function, Linear Regression theory that these models implement

How to use this vault section

Same skeleton throughout: Definition → Syntax → Key Parameters → Examples → Notes/Gotchas. Ctrl/Cmd+O and type “Sklearn” to jump between notes.