Choosing the Right Machine Learning Algorithm

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📊 Classification

📈 Regression

🔍 Clustering

📉 Dimensionality Reduction

🔍 Assess Interpretability vs. Performance

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📋 Small Dataset (< 10K samples) Simpler models work better

• Linear/Logistic Regression
• Decision Trees
• SVM
• K-Nearest Neighbors

🗃️ Large Dataset (> 100K samples) Complex models can excel

• Neural Networks
• Deep Learning
• Ensemble Methods
• Gradient Boosting

⚡ Performance Priority

• Random Forest
• Gradient Boosting
• Deep Learning
• XGBoost

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⚙️

💻 Low Resources

🖥️ Medium Resources

⚡ High Resources

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🧪

🔄 Try Several Approaches

✅ Use Cross-Validation

Random Forest

Boosting

Stacking

Bagging

Voting

Blending

Combine multiple models for improved accuracy and robustness

Start Simple: Begin with baseline models (Linear Regression, Logistic Regression) before trying complex algorithms.

Feature Engineering: Good features often matter more than complex algorithms. Clean and engineer your data first.

Domain Knowledge: Consider domain-specific constraints (regulatory requirements, real-time predictions, etc.).

Evaluation Metrics: Choose the right metrics for your problem (Accuracy, Precision, Recall, F1, AUC, RMSE, etc.).