📚 Important Machine Learning Concepts

Let's break down the key ideas you must master! 💡

🧹 Data Cleaning

Removing errors and inconsistencies in the dataset to ensure accurate models.

⚖️ Bias and Variance

Bias: Errors from wrong assumptions. Variance: Errors from sensitivity to small changes in data.

📊 Overfitting and Underfitting

Overfitting = model learns too much noise. Underfitting = model fails to learn enough patterns.

📈 Training and Testing Split

Separating data into training and testing sets to evaluate model performance properly.

🛠 Feature Engineering

Creating new features or modifying existing ones to improve model accuracy.

🚀 Model Evaluation Metrics

Metrics like Accuracy, Precision, Recall, and F1-Score measure how well your model performs.

✨ Quick Quiz!

What happens if a model learns the noise too much?

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By Darchums Technologies Inc - April 26, 2025