ALL SKLEARN MODELS (MAIN CATEGORIES)

 

✅ 1. Supervised Learning Models

📈 Regression (predict numbers)

• Linear Regression
• Ridge Regression
• Lasso Regression
• ElasticNet
• Decision Tree Regressor
• Random Forest Regressor
• Support Vector Regression (SVR)
• K-Nearest Neighbors Regressor

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📉 Classification (predict labels)

• Logistic Regression
• Decision Tree Classifier
• Random Forest Classifier
• Support Vector Machine (SVC)
• K-Nearest Neighbors (KNN)
• Naive Bayes:
  • GaussianNB
  • MultinomialNB
  • BernoulliNB
• SGD Classifier
• Perceptron
• Ridge Classifier

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🔍 2. Unsupervised Learning Models

📊 Clustering

• K-Means
• DBSCAN
• Agglomerative Clustering
• Mean Shift
• Spectral Clustering
• Birch

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📉 Dimensionality Reduction

• PCA (Principal Component Analysis)
• Kernel PCA
• Truncated SVD
• t-SNE (for visualization)
• Factor Analysis

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🔎 Outlier Detection

• One-Class SVM
• Isolation Forest
• Local Outlier Factor (LOF)

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⚙️ 3. Model Selection & Optimization (not models, but important tools)

• GridSearchCV
• RandomizedSearchCV
• cross_val_score
• train_test_split

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🧾 4. Ensemble Models (very important)

• BaggingClassifier / BaggingRegressor
• RandomForestClassifier / Regressor
• AdaBoost
• GradientBoostingClassifier / Regressor
• VotingClassifier
• StackingClassifier

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📊 5. Linear & Generalized Models

• LinearRegression
• LogisticRegression
• Ridge / Lasso / ElasticNet
• SGDRegressor / SGDClassifier
• Bayesian Ridge

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🧠 6. Probabilistic Models

• Naive Bayes (all variants)
• Gaussian Process Regression
• Gaussian Process Classification

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🔧 7. Other Useful ML Tools (not models)

• StandardScaler
• MinMaxScaler
• LabelEncoder
• OneHotEncoder
• PolynomialFeatures

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🧠 SIMPLE STRUCTURE

📈 Supervised Learning

→ Regression + Classification models

🔍 Unsupervised Learning

→ Clustering + Dimensionality Reduction

🔥 Ensemble Learning

→ Combining multiple models

⚙️ Utility Tools

→ Preprocessing + tuning