Why engineered features improve models in ML Python - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a new feature by combining two existing features.

ML Python

data['new_feature'] = data['feature1'] [1] data['feature2']

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

A+

B*

C-

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using subtraction or division which may not combine features meaningfully.

Forgetting to create a new feature column.

Adding two features creates a new combined feature that can capture more information.

2fill in blank

medium

Complete the code to scale features before training the model.

ML Python

from sklearn.preprocessing import {{BLANK_1 }}
scaler = [1]()
data_scaled = scaler.fit_transform(data)

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AOneHotEncoder

BMinMaxScaler

CStandardScaler

DLabelEncoder

Attempts:

3 left

💡 Hint

Common Mistakes

Using LabelEncoder which is for categorical labels.

Using OneHotEncoder which is for categorical variables.

StandardScaler standardizes features by removing the mean and scaling to unit variance.

3fill in blank

hard

Fix the error in the code to select only numeric features for feature engineering.

ML Python

numeric_features = data.select_dtypes(include=[1])

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A'category'

B'number'

C'numeric'

D'object'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'object' or 'category' which select non-numeric columns.

Using 'numeric' which is not a valid dtype string.

The correct dtype to select numeric columns is 'number'.

4fill in blank

hard

Fill both blanks to create a dictionary of feature lengths for words longer than 3 characters.

ML Python

lengths = {word: [1] for word in words if len(word) [2] 3 }

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Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Using less than (<) instead of greater than (>) in the condition.

We want the length of each word (len(word)) and only include words longer than 3 (len(word) > 3).

5fill in blank

hard

Fill all three blanks to filter features and create a dictionary of uppercase keys with values greater than zero.

ML Python

result = { [1]: [2] for k, v in data.items() if v [3] 0 }

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Ak.upper()

Bv

C>

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using k.lower() instead of k.upper().

Using less than (<) instead of greater than (>) in the condition.

Keys are converted to uppercase (k.upper()), values are kept as v, and only items with values greater than zero (v > 0) are included.