First ML prediction (linear regression) in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a simple linear regression model using scikit-learn.

ML Python

from sklearn.linear_model import [1]
model = LinearRegression()

Drag options to blanks, or click blank then click option'

ADecisionTreeClassifier

BLogisticRegression

CLinearRegression

DKMeans

Attempts:

3 left

💡 Hint

Common Mistakes

Using LogisticRegression which is for classification.

Using clustering models like KMeans.

The correct class to create a linear regression model is LinearRegression from sklearn.linear_model.

2fill in blank

medium

Complete the code to fit the linear regression model with features X and target y.

ML Python

model.[1](X, y)

Drag options to blanks, or click blank then click option'

Apredict

Bfit

Ctransform

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict before training the model.

Using transform which is for data preprocessing.

The fit method trains the model using the features X and target y.

3fill in blank

hard

Fix the error in the code to predict target values using the trained model.

ML Python

predictions = model.[1](X_test)

Drag options to blanks, or click blank then click option'

Afit

Bscore

Ctransform

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit again instead of predict.

Using score which returns a metric, not predictions.

To get predictions from the model, use the predict method on the test features.

4fill in blank

hard

Fill both blanks to calculate the mean squared error between true and predicted values.

ML Python

from sklearn.metrics import [1]
mse = [2](y_true, y_pred)

Drag options to blanks, or click blank then click option'

Amean_squared_error

Baccuracy_score

Cmean_absolute_error

Dr2_score

Attempts:

3 left

💡 Hint

Common Mistakes

Using accuracy_score which is for classification.

Using mean_absolute_error which is a different error metric.

The function mean_squared_error calculates the average squared difference between true and predicted values.

5fill in blank

hard

Fill all three blanks to create a dictionary of predictions for each test sample with keys as sample indices.

ML Python

pred_dict = [1](i: [2] for i, [3] in enumerate(y_pred))

Drag options to blanks, or click blank then click option'

Adict

Bvalue

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong variable names in the comprehension.

Not using dict() to create the dictionary.

The dict function creates a dictionary. The comprehension uses i as key and value as value from enumerated predictions.

Practice

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

easy

A. To classify data into categories

B. To find a line that best fits the data points

C. To cluster data points into groups

D. To reduce the number of features

Solution

1. Step 1: Understand linear regression purpose

   Linear regression aims to find a straight line that best fits the relationship between input and output data.

2. Step 2: Compare with other options

   Classification, clustering, and feature reduction are different tasks, not the goal of linear regression.

3. Final Answer:

   To find a line that best fits the data points -> Option B

4. Quick Check:

   Linear regression = line fitting [OK]

Hint: Linear regression fits a line, not groups or 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 Python syntax to create a simple linear regression model using scikit-learn?

easy

A. model = LinearRegression()

B. model = LinearRegression.fit()

C. model = LinearRegression.fit(X, y)

D. model = LinearRegression(X, y)

Solution

1. Step 1: Recall model creation syntax

   In scikit-learn, you first create the model object with LinearRegression(), without arguments.

2. Step 2: Understand fitting method

   The fit method is called on the model object with data, e.g., model.fit(X, y), not during creation.

3. Final Answer:

   model = LinearRegression() -> Option A

4. Quick Check:

   Model creation uses class name with parentheses [OK]

Hint: Create model with class(), fit separately [OK]

Common Mistakes:

• Calling fit during model creation
• Passing data to constructor
• Using fit as a class method

3. Given the code below, what will be the predicted value for X_new = [[5]]?

from sklearn.linear_model import LinearRegression
X = [[1], [2], [3], [4]]
y = [2, 4, 6, 8]
model = LinearRegression()
model.fit(X, y)
prediction = model.predict([[5]])
print(prediction)

medium

A. [9.]

B. [12.]

C. [8.]

D. [10.]

Solution

1. Step 1: Understand the training data pattern

   The output y doubles the input X: y = 2 * X.

2. Step 2: Predict for X_new = 5

   Using the pattern, prediction = 2 * 5 = 10.

3. Final Answer:

   [10.] -> Option D

4. Quick Check:

   2 x 5 = 10 [OK]

Hint: Check pattern in training data, apply to new input [OK]

Common Mistakes:

• Ignoring the pattern and guessing
• Using last y value as prediction
• Confusing input and output

4. What is wrong with the following code snippet for training a linear regression model?

from sklearn.linear_model import LinearRegression
X = [1, 2, 3, 4]
y = [2, 4, 6, 8]
model = LinearRegression()
model.fit(X, y)

medium

A. X should be a 2D array, not a 1D list

B. y should be a 2D array, not a list

C. LinearRegression() requires parameters in constructor

D. fit() method should be called with no arguments

Solution

1. Step 1: Check input shape requirements

   scikit-learn expects X as a 2D array (list of lists), but X is a 1D list here.

2. Step 2: Confirm y shape and method usage

   y can be 1D list, constructor needs no parameters, fit requires X and y arguments.

3. Final Answer:

   X should be a 2D array, not a 1D list -> Option A

4. Quick Check:

   X must be 2D for fit() [OK]

Hint: X must be 2D array for sklearn fit() [OK]

Common Mistakes:

• Passing 1D list for X
• Thinking y must be 2D
• Adding parameters to LinearRegression()

5. You trained a linear regression model on data where y = 3x + 2. After training, you want to predict the output for x = 10. Which code snippet correctly predicts and prints the result?

hard

A. model.predict([10])\nprint(prediction)

B. prediction = model.predict([10, 2])\nprint(prediction)

C. prediction = model.predict([[10]])\nprint(prediction[0])

D. prediction = model.predict(10)\nprint(prediction)

Solution

1. Step 1: Understand input shape for prediction

   scikit-learn expects a 2D array for prediction input, so [[10]] is correct.

2. Step 2: Access prediction output correctly

   predict returns an array; to print the single value, use prediction[0].

3. Final Answer:

   prediction = model.predict([[10]]) print(prediction[0]) -> Option C

4. Quick Check:

   Input 2D array, print first prediction [OK]

Hint: Use 2D input for predict, print first element [OK]

Common Mistakes:

• Passing 1D list or scalar to predict
• Printing prediction array directly without indexing
• Passing wrong shaped input with extra values