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Summary - Regression

In this group you learned linear regression, model training, and prediction.

Summary: regression

In this group:

  • Regression predicts a continuous number; models are named ...Regressor.
  • Linear regression fits the line y = w·x + b by least squares; coef_ and intercept_ are interpretable.
  • fit learns the parameters, predict predicts with two-dimensional input.
  • It is measured with MAE, MSE, RMSE and R²; measuring on test data is essential.
m = LinearRegression().fit(X_train, y_train)
p = m.predict(X_test)
print(mean_absolute_error(y_test, p), r2_score(y_test, p))

Practice End-to-end regression

Split the data with test_size=0.25, random_state=0 and train a LinearRegression. On the test set, print the MAE with round(..., 2) and the R² with round(..., 3) on separate lines.

Starter code

model.py
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_absolute_error, r2_score

rng = np.random.default_rng(0)
X = rng.uniform(0, 10, size=(40, 1))
y = 4 * X[:, 0] + 3 + rng.normal(0, 1, size=40)

# buraya yaz

Sample solution

model.py
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_absolute_error, r2_score

rng = np.random.default_rng(0)
X = rng.uniform(0, 10, size=(40, 1))
y = 4 * X[:, 0] + 3 + rng.normal(0, 1, size=40)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)
m = LinearRegression().fit(X_train, y_train)
p = m.predict(X_test)
print(round(mean_absolute_error(y_test, p), 2))
print(round(r2_score(y_test, p), 3))

Expected output

0.94
0.99

Questions and answers

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

1 What can be said of a model with an R² of -0.2?

  • It is very good
  • The error is 20%
  • R² can't be negative
  • It is worse than always predicting the mean Correct answer

Explanation: A negative R² shows the model makes more error than the simple mean prediction.

2 Get R² on the test set directly from the model.

Question 
r2 = m.____(X_test, y_test)
Answer 
r2 = m.score(X_test, y_test)

Explanation: For regression models score returns R².

3 Match the tools.

Term Meaning
LinearRegression Linear model
coef_ Weights
mean_squared_error MSE
r2_score R²

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