Training data and models in Intro to Computing - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load training data from a CSV file using pandas.

Intro to Computing

import pandas as pd
train_data = pd.read_csv([1])

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

A'train.csv'

Btrain.csv

Ccsv.read('train.csv')

Dpd.load('train.csv')

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting quotes around the filename.

Using incorrect function names like pd.load or csv.read.

The filename must be a string inside quotes when using pd.read_csv().

2fill in blank

medium

Complete the code to split data into features and labels.

Intro to Computing

X = data.drop(columns=[1])
y = data['label']

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

A'label'

Blabel

C['label']

Dlabel_column

Attempts:

3 left

💡 Hint

Common Mistakes

Not using quotes around the column name.

Using a variable name instead of the string.

The column name must be a string inside quotes when dropping it.

3fill in blank

hard

Fix the error in the code to train a model using scikit-learn.

Intro to Computing

from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.[1](X, y)

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

Apredict

Btrain

Ctransform

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using train() instead of fit().

Using predict() before training the model.

The method to train a model in scikit-learn is fit().

4fill in blank

hard

Fill both blanks to create a dictionary of feature means for training data where values are greater than 0.

Intro to Computing

feature_means = {col: data[col].[1]() for col in data.columns if (data[col][2] 0).any()}

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

Amean

Bmax

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using max() instead of mean().

Using < instead of > in the condition.

We calculate the mean of each column and filter columns where values are greater than 0.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps feature names to their max values if max is greater than 10.

Intro to Computing

max_values = { [1] : data[[2]].[3]() for [2] in data.columns if data[[2]].max() > 10 }

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

Afeature

Bcol

Cmax

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean() instead of max().

Using an undefined variable like 'feature' for the key.

We use 'col' as key, iterate with 'col', and use max() to get max values.

Practice

1. What is the main purpose of training data in machine learning?

easy

A. To teach the model patterns and relationships in data

B. To test the model's accuracy after training

C. To store the final predictions of the model

D. To clean and prepare data for analysis

Solution

1. Step 1: Understand the role of training data

   Training data is used to show the model examples so it can learn patterns.

2. Step 2: Differentiate training from testing

   Testing data checks model accuracy, but training data teaches the model.

3. Final Answer:

   To teach the model patterns and relationships in data -> Option A

4. Quick Check:

   Training data = Teach model [OK]

Hint: Training data is what the model learns from [OK]

Common Mistakes:

• Confusing training data with testing data
• Thinking training data stores predictions
• Assuming training data is for cleaning only

2. Which of the following is the correct way to split data into training and testing sets in Python using scikit-learn?

easy

A. test_train_split(data, size=0.2)

B. train_test_split(data, test_size=0.2)

C. train_test(data, split=0.2)

D. split_train_test(data, 0.2)

Solution

1. Step 1: Recall the scikit-learn function name

   The correct function to split data is train_test_split.

2. Step 2: Check the parameter syntax

   The parameter test_size=0.2 correctly sets 20% data for testing.

3. Final Answer:

   train_test_split(data, test_size=0.2) -> Option B

4. Quick Check:

   Correct function and parameter = train_test_split(data, test_size=0.2) [OK]

Hint: Remember scikit-learn's function is train_test_split [OK]

Common Mistakes:

• Using wrong function names
• Mixing parameter names
• Incorrect order of arguments

3. Given the following Python code, what will be the output?

from sklearn.linear_model import LinearRegression
import numpy as np

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

medium

A. 9.0

B. 12.0

C. 8.0

D. 10.0

Solution

1. Step 1: Understand the training data relationship

   X values are 1 to 4, y values are double X (2,4,6,8), so model learns y = 2 * X.

2. Step 2: Predict for X=5 using the model

   Prediction = 2 * 5 = 10.0, rounding to 1 decimal is 10.0.

3. Final Answer:

   10.0 -> Option D

4. Quick Check:

   Prediction for 5 = 10.0 [OK]

Hint: Model learns y=2x, predict for 5 gives 10 [OK]

Common Mistakes:

• Ignoring rounding in output
• Misunderstanding model relationship
• Using wrong input shape for predict

4. Identify the error in this code snippet for training a model:

from sklearn.linear_model import LogisticRegression
X_train = [[0, 1], [1, 0]]
y_train = [0, 1]
model = LogisticRegression()
model.fit(y_train, X_train)

medium

A. The order of arguments in model.fit() is reversed

B. LogisticRegression cannot be used with this data

C. X_train should be a numpy array, not a list

D. y_train should contain strings, not integers

Solution

1. Step 1: Check the model.fit() argument order

   fit() expects features first (X_train), then labels (y_train).

2. Step 2: Identify the mistake in the code

   Code passes y_train first, then X_train, which is incorrect and causes error.

3. Final Answer:

   The order of arguments in model.fit() is reversed -> Option A

4. Quick Check:

   fit(X_train, y_train) correct order [OK]

Hint: fit() needs features first, labels second [OK]

Common Mistakes:

• Swapping X and y in fit()
• Assuming data type causes error here
• Thinking LogisticRegression needs string labels

5. You have a dataset with 1000 samples and 10 features. You want to train a model but avoid overfitting. Which approach helps most during training?

hard

A. Use all data for training without splitting

B. Train only on the first 100 samples

C. Split data into training and validation sets

D. Ignore feature scaling and train directly

Solution

1. Step 1: Understand overfitting and validation

   Overfitting happens when model learns training data too well and fails on new data.

2. Step 2: Use validation set to check model generalization

   Splitting data into training and validation helps detect and reduce overfitting.

3. Final Answer:

   Split data into training and validation sets -> Option C

4. Quick Check:

   Validation split prevents overfitting [OK]

Hint: Validation set helps check model generalization [OK]

Common Mistakes:

• Training on all data hides overfitting
• Using too few samples reduces learning
• Ignoring feature scaling can hurt model