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Accuracy

Accuracy is the most common classification metric.

Accuracy and error rate

Accuracy = (TP + TN) / total. Its opposite is the error rate: 1 - accuracy. It is computed directly from the confusion matrix:

cm = confusion_matrix(y_true, y_pred)
dogruluk = np.trace(cm) / cm.sum()   # diagonal / total

np.trace is the sum of the diagonal elements; in a multi-class matrix it also gives the number of correct predictions.

Balanced accuracy

On imbalanced data balanced accuracy is the mean of each class's accuracy within itself; a model predicting the majority class gets 0.5:

from sklearn.metrics import balanced_accuracy_score
y_true = [0] * 9 + [1]
y_pred = [0] * 10
print(accuracy_score(y_true, y_pred))           # 0.9
print(balanced_accuracy_score(y_true, y_pred))  # 0.5

If imbalanced data must be summarised with a single number, it is more honest than plain accuracy.

Practice Two accuracies

For the given labels, print accuracy_score and balanced_accuracy_score with round(..., 3) on separate lines.

Starter code

model.py
from sklearn.metrics import accuracy_score, balanced_accuracy_score

y_true = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1]
y_pred = [0, 0, 0, 0, 0, 0, 0, 0, 1, 0]

# buraya yaz

Sample solution

model.py
from sklearn.metrics import accuracy_score, balanced_accuracy_score

y_true = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1]
y_pred = [0, 0, 0, 0, 0, 0, 0, 0, 1, 0]

print(round(accuracy_score(y_true, y_pred), 3))
print(round(balanced_accuracy_score(y_true, y_pred), 3))

Expected output

0.9
0.75

Questions and answers

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

1 If TP=40, TN=45, FP=5, FN=10, what is the accuracy?

  • 0.45
  • 0.85 Correct answer
  • 0.80
  • 0.90

Explanation: (40 + 45) / 100 = 0.85.

2 Take the diagonal sum of the matrix.

Question 
dogru = np.____(cm)
Answer 
dogru = np.trace(cm)

Explanation: trace adds up the diagonal elements.

3 Match the metrics.

Term Meaning
accuracy (TP+TN)/total
error rate 1 - accuracy
balanced accuracy Mean of class accuracies
np.trace(cm) Number of correct predictions

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