Why regression predicts continuous values in ML Python - Test Your Understanding

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 = [1]()

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

ADecisionTreeClassifier

BLogisticRegression

CKNeighborsClassifier

DLinearRegression

Attempts:

3 left

💡 Hint

Common Mistakes

Using classification models like LogisticRegression for regression tasks.

Confusing model names that end with 'Classifier' instead of 'Regression'.

The LinearRegression class is used to create regression models that predict continuous values.

2fill in blank

medium

Complete the code to fit the regression model on training data.

ML Python

model.fit(X_train, [1])

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Ay_train

BX_test

Cy_test

DX_train

Attempts:

3 left

💡 Hint

Common Mistakes

Passing test data instead of training data to fit.

Confusing features and target variables.

The fit method trains the model using input features X_train and target values y_train.

3fill in blank

hard

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

ML Python

predictions = model.[1](X_test)

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Apredict

Bpredict_proba

Cscore

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict_proba which is for classification probabilities.

Calling fit again instead of predicting.

The predict method returns continuous predicted values for regression models.

4fill in blank

hard

Fill both blanks to calculate the mean squared error (MSE) for regression predictions.

ML Python

from sklearn.metrics import [1]
mse = [2](y_test, predictions)

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Amean_squared_error

Baccuracy_score

Cconfusion_matrix

Droc_auc_score

Attempts:

3 left

💡 Hint

Common Mistakes

Using classification metrics like accuracy_score for regression.

Confusing metric names.

mean_squared_error is the correct metric to measure error for continuous predictions in regression.

5fill in blank

hard

Fill all three blanks to create a dictionary of predictions for each test sample with their true values.

ML Python

results = [1](zip([2], [3]))

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Adict

By_test

Cpredictions

Dlist

Attempts:

3 left

💡 Hint

Common Mistakes

Using list instead of dict to store pairs.

Swapping true values and predictions in zip.

Using dict with zip(y_test, predictions) pairs true values with predicted values in a dictionary.

Practice

1. Why does regression predict continuous values instead of categories?

easy

A. Because it predicts yes or no answers

B. Because it groups data into fixed classes

C. Because it only works with text data

D. Because it estimates quantities that can take any value within a range

Solution

1. Step 1: Understand regression's purpose

   Regression is designed to predict numbers that can vary continuously, like prices or temperatures.

2. Step 2: Compare with classification

   Classification predicts fixed groups or categories, not continuous values.

3. Final Answer:

   Because it estimates quantities that can take any value within a range -> Option D

4. Quick Check:

   Regression predicts continuous values = Because it estimates quantities [OK]

Hint: Regression predicts numbers, classification predicts groups [OK]

Common Mistakes:

• Confusing regression with classification
• Thinking regression predicts categories
• Assuming regression only works with discrete values

2. Which of the following is the correct way to define a regression target variable in Python?

easy

A. target = [3.5, 7.2, 1.8]

B. target = ['cat', 'dog', 'bird']

C. target = ['red', 'blue', 'green']

D. target = ['yes', 'no', 'maybe']

Solution

1. Step 1: Identify regression target type

   Regression targets are continuous numbers, like 3.5 or 7.2.

2. Step 2: Check options for continuous values

   Only target = [3.5, 7.2, 1.8] contains numbers suitable for regression.

3. Final Answer:

   target = [3.5, 7.2, 1.8] -> Option A

4. Quick Check:

   Regression targets are numbers = target with floats [OK]

Hint: Regression targets are numeric lists, not categories [OK]

Common Mistakes:

• Using categorical strings as regression targets
• Confusing classification labels with regression targets
• Using boolean or text values for regression

3. You want to predict house prices using regression. Your dataset has features like size (sq ft), number of rooms, and age of the house. Why is regression the right choice here?

easy

A. Because house prices belong to fixed categories like cheap or expensive

B. Because house prices are continuous values that can vary widely

C. Because regression only works with text data

D. Because regression predicts yes/no answers

Solution

1. Step 1: Identify target variable type

   House prices are numbers that can take many values, not fixed groups.

2. Step 2: Match problem to regression

   Regression predicts continuous numeric values, perfect for price prediction.

3. Final Answer:

   Because house prices are continuous values that can vary widely -> Option B

4. Quick Check:

   Price prediction = continuous value regression [OK]

Hint: Predict continuous numbers like prices with regression [OK]

Common Mistakes:

• Treating prices as categories
• Choosing classification instead of regression
• Confusing regression with yes/no prediction

4. Given this simple regression model in Python using scikit-learn:

from sklearn.linear_model import LinearRegression
X = [[1], [2], [3], [4]]
y = [2.0, 4.1, 6.0, 8.1]
model = LinearRegression()
model.fit(X, y)
pred = model.predict([[5]])
print(round(pred[0], 1))

What will be the printed output?

medium

A. 10.1

B. 8.5

C. 9.9

D. 10.0

Solution

1. Step 1: Understand the data and model

   The data roughly follows y = 2 * x with small noise. The model fits a line close to y=2x.

2. Step 2: Predict for x=5

   Prediction is about 2 * 5 = 10, but slight noise shifts it to around 10.1.

3. Final Answer:

   10.1 -> Option A

4. Quick Check:

   Linear fit predicts ~10.1 for input 5 [OK]

Hint: Regression predicts near trend line values, not exact multiples [OK]

Common Mistakes:

• Assuming exact multiples without noise
• Confusing classification output with regression output
• Rounding incorrectly

5. You wrote this regression code but get an error:

from sklearn.linear_model import LinearRegression
X = [1, 2, 3, 4]
y = [2.0, 4.1, 6.0, 8.1]
model = LinearRegression()
model.fit(X, y)

What is the error and how to fix it?

medium

A. model.fit needs no arguments, remove X and y

B. y should be strings, fix with y = ['2.0', '4.1', '6.0', '8.1']

C. X should be a 2D array, fix with X = [[1], [2], [3], [4]]

D. LinearRegression only works with categorical data

Solution

1. Step 1: Identify input shape error

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

2. Step 2: Fix input shape

   Wrap each value in a list: X = [[1], [2], [3], [4]] to make it 2D.

3. Final Answer:

   X should be a 2D array, fix with X = [[1], [2], [3], [4]] -> Option C

4. Quick Check:

   Input X must be 2D array for regression [OK]

Hint: Regression input X must be 2D array, not 1D list [OK]

Common Mistakes:

• Passing 1D list instead of 2D array for X
• Changing y to strings incorrectly
• Removing required fit arguments