Bias vs variance
Bias is error from overly simple assumptions; variance is sensitivity to training data noise.
- High bias misses pattern
- High variance chases noise
- Need a balance
Bias vs variance
Machine Learning cards
16 cards
Classification vs regression
Classification predicts categories; regression predicts continuous numeric values.
- Class labels vs numbers
- Metrics differ
- Thresholding often matters for classification
Classification vs regression
Model monitoring basics
Model monitoring tracks data drift, prediction quality, latency, and operational health after deployment.
- Watch for drift
- Track business metrics too
- Retrain only when signals justify it
Model monitoring basics
Overfitting vs underfitting
Overfitting memorizes noise; underfitting is too simple to capture the real pattern.
- Overfit has low train and poor test performance
- Underfit performs poorly everywhere
- Control with data, features, and regularization
Overfitting vs underfitting
Precision vs recall vs F1
Precision asks how many predicted positives were right, recall asks how many real positives were found, and F1 balances both.
- Precision avoids false positives
- Recall avoids false negatives
- F1 is harmonic mean
Precision vs recall vs F1
Supervised vs unsupervised learning
Supervised learning uses labeled targets, while unsupervised learning looks for structure without target labels.
- Supervised predicts known target
- Unsupervised finds patterns
- Examples: classification vs clustering
Supervised vs unsupervised learning
What is a confusion matrix?
A confusion matrix counts true and false predictions by actual and predicted class.
- TP FP FN TN for binary case
- Helps derive precision and recall
- Great for error analysis
What is a confusion matrix?
What is a learning rate?
The learning rate controls how large each parameter update step is during training.
- Too high can overshoot
- Too low learns slowly
- Often tuned experimentally
What is a learning rate?
What is data leakage in machine learning?
Data leakage happens when training uses information that would not be available at real prediction time.
- Inflates evaluation scores
- Common in preprocessing mistakes
- Think carefully about time and target leakage
What is data leakage in machine learning?
What is dimensionality reduction?
Dimensionality reduction compresses features into fewer dimensions while keeping as much useful structure as possible.
- Can help visualization
- May reduce noise
- PCA is a common example
What is dimensionality reduction?