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Summary - Metrics

In this group you learned accuracy, precision, recall, and F1 metrics.

Summary: metrics

In this group you deepened the core metrics:

  • Loss is minimised in training, a metric is interpreted; log-loss measures probability quality.
  • Per-class accuracy and top-k accuracy with many classes.
  • macro, weighted, micro averages; micro equals accuracy in single-label problems.
  • The ROC curve and threshold-independent AUC.
  • Adjusting the precision-recall balance with F-beta; choosing the threshold on validation data.

Practice The best F1 threshold

Run precision_recall_curve for probabilities p and labels y, compute each threshold's F1 and print the threshold giving the highest F1.

Starter code

model.py
import numpy as np
from sklearn.metrics import precision_recall_curve

y = np.array([0, 0, 1, 0, 1, 1, 0, 1])
p = np.array([0.1, 0.3, 0.35, 0.4, 0.6, 0.7, 0.75, 0.9])

# buraya yaz

Sample solution

model.py
import numpy as np
from sklearn.metrics import precision_recall_curve

y = np.array([0, 0, 1, 0, 1, 1, 0, 1])
p = np.array([0.1, 0.3, 0.35, 0.4, 0.6, 0.7, 0.75, 0.9])

pr, re, esik = precision_recall_curve(y, p)
f1 = 2 * pr * re / (pr + re + 1e-12)
print(esik[f1[:-1].argmax()])

Expected output

0.35

Questions and answers

Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.

1 Which fits comparing the ranking power of two classifiers without choosing a threshold?

  • F1
  • Precision
  • ROC AUC Correct answer
  • Accuracy

Explanation: AUC evaluates all thresholds together.

2 Get multi-class F1 with equal weight for classes.

Question 
f1_score(y_true, y_pred, average=____)
Answer 
f1_score(y_true, y_pred, average="macro")

Explanation: "macro" treats small classes as importantly as large ones.

3 Match the metrics.

Term Meaning
log_loss Probability quality
top_k_accuracy_score Correct within top k
roc_auc_score Ranking power
fbeta_score Adjustable balance

All lessons in Beginner

1 What is ML?

  1. What is machine learning?
  2. Supervised and unsupervised
  3. Data and model
  4. Training and testing
  5. Feature and label notation
  6. Summary - ML fundamentals

2 NumPy basics

  1. What is NumPy?
  2. Creating arrays
  3. Array properties
  4. Array indexing
  5. Reshaping arrays
  6. Summary - NumPy basics

3 Pandas basics

  1. What is Pandas?
  2. Creating a DataFrame
  3. Column operations
  4. Selecting and filtering data
  5. Viewing first rows
  6. Summary - Pandas basics

4 Data preprocessing

  1. Data cleaning
  2. Missing data
  3. Data types
  4. Label encoding
  5. Data splitting
  6. Summary - Data preprocessing

5 Statistics basics

  1. Mean
  2. Median
  3. Standard deviation
  4. Variance
  5. Correlation
  6. Summary - Statistics

6 Linear algebra

  1. Vectors
  2. Matrices
  3. Matrix multiplication
  4. Transpose
  5. Identity matrix
  6. Summary - Linear algebra

7 Project: Data analysis

  1. Loading data
  2. Cleaning data
  3. Computing statistics
  4. Printing results

8 Regression

  1. What is regression?
  2. Linear regression
  3. Model training
  4. Prediction
  5. Error measurement
  6. Summary - Regression

9 KNN

  1. What is KNN?
  2. The K parameter
  3. Distance measurement
  4. Classification
  5. Model training
  6. Summary - KNN

10 Decision trees

  1. What is a decision tree?
  2. Splitting criterion
  3. Depth
  4. Classification
  5. Overfitting
  6. Summary - Decision trees

11 Classification

  1. What is classification?
  2. Binary classification
  3. Multi-class
  4. Accuracy
  5. Confusion matrix
  6. Summary - Classification

12 Model evaluation

  1. Train-test split
  2. Accuracy
  3. Precision
  4. Recall
  5. F1 score
  6. Summary - Model evaluation

13 Project: Classification

  1. Preparing data
  2. Training model
  3. Prediction
  4. Evaluation

14 Scaling

  1. What is scaling?
  2. StandardScaler
  3. MinMaxScaler
  4. When to use?
  5. Pipeline
  6. Summary - Scaling

15 Feature engineering

  1. What is a feature?
  2. Feature selection
  3. Feature extraction
  4. Categorical features
  5. Numerical features
  6. Summary - Feature engineering

16 Model selection

  1. What is model selection?
  2. Cross-validation
  3. Grid search
  4. Score comparison
  5. Hyperparameter
  6. Summary - Model selection

17 Project: Regression

  1. Preparing data
  2. Training model
  3. Prediction
  4. Error measurement

18 Metrics

  1. What is a metric?
  2. Accuracy
  3. Precision
  4. Recall
  5. F1 score
  6. Summary - Metrics

19 Data visualization

  1. What is visualization?
  2. Scatter plot
  3. Histogram
  4. Line plot
  5. Box plot
  6. Summary - Visualization

20 Beginner final

  1. Challenge 1
  2. Challenge 2
  3. Challenge 3
  4. Challenge 4