Linear regression with scikit-learn in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main goal of linear regression?

The main goal of linear regression is to find a straight line that best fits the relationship between input features and a continuous output, so it can predict new values.

beginner

Which scikit-learn class is used to perform linear regression?

The class LinearRegression from sklearn.linear_model is used to create and train a linear regression model.

beginner

What does the fit() method do in scikit-learn's LinearRegression?

The fit() method trains the model by finding the best line that fits the input data and output values.

intermediate

How do you evaluate the performance of a linear regression model in scikit-learn?

You can use the score() method to get the R² score, which shows how well the model explains the data (1 is perfect, 0 means no explanation).

beginner

What is the difference between predict() and fit() methods?

fit() trains the model using data, while predict() uses the trained model to estimate outputs for new input data.

Which method in scikit-learn LinearRegression is used to train the model?

A fit()

B predict()

C score()

D transform()

The fit() method trains the model by finding the best line for the data.

What does the R² score represent in linear regression?

A The percentage of data points correctly classified

B How well the model explains the variance in the data

C The number of features used in the model

D The speed of model training

R² score measures how well the model explains the variance in the output data.

Which scikit-learn module contains the LinearRegression class?

A sklearn.linear_model

B sklearn.cluster

C sklearn.preprocessing

D sklearn.metrics

LinearRegression is part of the sklearn.linear_model module.

What type of problem is linear regression used for?

A Classification

B Clustering

C Regression

D Dimensionality reduction

Linear regression is used for regression problems where the output is continuous.

After training a linear regression model, which method predicts new outputs?

A fit_predict()

B fit()

C score()

D predict()

The predict() method uses the trained model to estimate outputs for new inputs.

Explain the steps to train and evaluate a linear regression model using scikit-learn.

Think about how you prepare, train, predict, and check the model.

Describe what the R² score tells you about a linear regression model's performance.

It's about how much of the data's variation the model captures.

Practice

1. What is the main goal of linear regression in machine learning?

easy

A. To find a line that predicts a continuous value from input data

B. To classify data into categories

C. To cluster data points into groups

D. To reduce the number of features in data

Solution

1. Step 1: Understand the purpose of linear regression

   Linear regression aims to model the relationship between input variables and a continuous output by fitting a line.

2. Step 2: Compare with other tasks

   Classification assigns categories, clustering groups data, and feature reduction simplifies data, which are different tasks.

3. Final Answer:

   To find a line that predicts a continuous value from input data -> Option A

4. Quick Check:

   Linear regression predicts continuous values [OK]

Hint: Linear regression predicts continuous numbers, not categories [OK]

Common Mistakes:

• Confusing regression with classification
• Thinking linear regression clusters data
• Believing it reduces features

2. Which of the following is the correct way to import the LinearRegression class from scikit-learn?

easy

A. from sklearn.linear_model import LinearRegression

B. import LinearRegression from sklearn.linear_model

C. from sklearn import LinearRegression

D. import linear_model.LinearRegression

Solution

1. Step 1: Recall correct Python import syntax

   Python imports classes using 'from module import ClassName' syntax.

2. Step 2: Match with scikit-learn structure

   LinearRegression is inside sklearn.linear_model, so correct import is from sklearn.linear_model import LinearRegression.

3. Final Answer:

   from sklearn.linear_model import LinearRegression -> Option A

4. Quick Check:

   Correct import syntax = from sklearn.linear_model import LinearRegression [OK]

Hint: Use 'from sklearn.linear_model import LinearRegression' [OK]

Common Mistakes:

• Using wrong import order
• Trying to import directly from sklearn
• Using incorrect module names

3. What will be the output of the following code snippet?

from sklearn.linear_model import LinearRegression
import numpy as np

X = np.array([[1], [2], [3], [4], [5]])
y = np.array([2, 4, 6, 8, 10])
model = LinearRegression()
model.fit(X, y)
print(round(model.coef_[0], 1))

medium

A. 0.5

B. 2.0

C. 10.0

D. 1.0

Solution

1. Step 1: Understand the data relationship

   X values are 1 to 5, y values are exactly double X (2,4,6,8,10), so slope should be 2.

2. Step 2: Check model coefficient

   LinearRegression fits y = coef * X + intercept. coef_ will be close to 2.0 here.

3. Final Answer:

   2.0 -> Option B

4. Quick Check:

   Slope of y=2x is 2.0 [OK]

Hint: Slope equals change in y over change in x [OK]

Common Mistakes:

• Confusing coefficient with intercept
• Not rounding output
• Assuming coefficient is 1 or 10

4. Identify the error in this code that tries to train a linear regression model:

from sklearn.linear_model import LinearRegression
X = [[1], [2], [3]]
y = [2, 4, 6]
model = LinearRegression()
model.fit(X, y)
print(model.predict([4]))

medium

A. Passing a list instead of numpy array to fit

B. LinearRegression class is not instantiated correctly

C. Missing import for numpy

D. Predict input shape is incorrect, should be 2D array

Solution

1. Step 1: Check input shapes for fit and predict

   fit accepts list of lists for X, so no error there. But predict expects 2D array, [4] is 1D.

2. Step 2: Fix predict input shape

   Predict should get [[4]] (2D) not [4] (1D) to avoid shape error.

3. Final Answer:

   Predict input shape is incorrect, should be 2D array -> Option D

4. Quick Check:

   Predict input must be 2D array [OK]

Hint: Always pass 2D array to predict, e.g., [[value]] [OK]

Common Mistakes:

• Passing 1D array to predict
• Assuming fit needs numpy arrays
• Wrong class instantiation

5. You trained a linear regression model with scikit-learn and got an R² score of 0.85 on test data. What does this score mean?

hard

A. The model has 85% chance to predict correctly

B. The model's predictions are 85% accurate

C. The model explains 85% of the variance in the target variable

D. The model's error rate is 15% on test data

Solution

1. Step 1: Understand R² score meaning

   R² (coefficient of determination) measures how well the model explains variance in the target.

2. Step 2: Interpret 0.85 R²

   A score of 0.85 means 85% of the variance in the target is explained by the model.

3. Final Answer:

   The model explains 85% of the variance in the target variable -> Option C

4. Quick Check:

   R² = variance explained, not accuracy or error rate [OK]

Hint: R² shows variance explained, not accuracy percentage [OK]

Common Mistakes:

• Confusing R² with accuracy
• Thinking R² is probability
• Assuming R² is error rate