Target encoding in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the mean target value for each category.

ML Python

mean_target = df.groupby('category')['target'].[1]()

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

Asum

Bmax

Cmean

Dcount

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum instead of mean

Using count which counts entries, not averages

The mean() function calculates the average target value for each category, which is the basis of target encoding.

2fill in blank

medium

Complete the code to map the target mean to the original dataframe's category column.

ML Python

df['category_encoded'] = df['category'].[1](mean_target)

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

Amap

Bapply

Creplace

Dfillna

Attempts:

3 left

💡 Hint

Common Mistakes

Using apply which is slower and less direct

Using replace which is for replacing values but not with a mapping dictionary

The map() function replaces each category with its corresponding mean target value from mean_target.

3fill in blank

hard

Fix the error in the code to avoid data leakage by computing target encoding only on the training set.

ML Python

mean_target_train = train_df.groupby('category')['target'].[1]()
train_df['category_encoded'] = train_df['category'].map(mean_target_train)
test_df['category_encoded'] = test_df['category'].map(mean_target_train).fillna([2])

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

Amean

Bsum

Ccount

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Calculating mean on the whole dataset causing leakage

Not filling missing values in test data causing NaNs

We use mean to calculate the average target in the training set. For categories in the test set not seen in training, we fill missing values with 0 to avoid errors.

4fill in blank

hard

Fill both blanks to create a target encoding function that fits on training data and transforms new data.

ML Python

def target_encode(train, test, cat_col, target_col):
    mean_target = train.groupby([1])[[2]].mean()
    train_encoded = train[cat_col].map(mean_target)
    test_encoded = test[cat_col].map(mean_target).fillna(mean_target.mean())
    return train_encoded, test_encoded

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

A'category'

B'target'

C'category_encoded'

D'target_encoded'

Attempts:

3 left

💡 Hint

Common Mistakes

Using encoded column names instead of original columns

Mixing up target and category column names

The function groups training data by the category column and calculates the mean of the target column. These are the inputs to groupby and the column to average.

5fill in blank

hard

Fill all three blanks to apply target encoding with smoothing to reduce noise for categories with few samples.

ML Python

def smooth_target_encode(train, test, cat_col, target_col, m=5):
    mean = train[target_col].mean()
    agg = train.groupby([1])[[2]].agg(['mean', 'count'])
    smooth = (agg['count'] * agg['mean'] + m * mean) / (agg['count'] + m)
    train_encoded = train[cat_col].map(smooth)
    test_encoded = test[cat_col].map(smooth).fillna(mean)
    return train_encoded, test_encoded

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

A'category'

B'target'

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping category and target columns in groupby or agg

Not filling missing values in test data

The groupby is done on the category column ('category'), and the aggregation is on the target column ('target'). The smoothing formula uses counts and means to balance category averages with the global mean.

Practice

1. What is the main purpose of target encoding in machine learning?

easy

A. Remove missing values from the dataset

B. Normalize numerical features to a 0-1 scale

C. Create new categorical features by combining existing ones

D. Convert categorical variables into numbers using the average target value

Solution

1. Step 1: Understand what target encoding does

   Target encoding replaces categories with the average value of the target variable for each category.

2. Step 2: Compare with other options

   Normalization scales numbers, missing value removal cleans data, and feature creation combines categories, none of which describe target encoding.

3. Final Answer:

   Convert categorical variables into numbers using the average target value -> Option D

4. Quick Check:

   Target encoding = average target per category [OK]

Hint: Target encoding uses target averages to convert categories [OK]

Common Mistakes:

• Confusing target encoding with normalization
• Thinking target encoding creates new categories
• Assuming target encoding removes missing data

2. Which of the following Python code snippets correctly applies target encoding using pandas for a training dataset train_df with categorical column cat_col and target target?

easy

A. mean_target = train_df.groupby('cat_col')['target'].mean(); train_df['cat_encoded'] = train_df['cat_col'].map(mean_target)

B. train_df['cat_encoded'] = train_df['cat_col'].astype('category').cat.codes

C. train_df['cat_encoded'] = train_df['target'].mean()

D. train_df['cat_encoded'] = train_df['cat_col'].apply(lambda x: len(x))

Solution

1. Step 1: Identify correct target encoding method

   Target encoding maps each category to the mean target value for that category, done by grouping and mapping.

2. Step 2: Check code correctness

   mean_target = train_df.groupby('cat_col')['target'].mean(); train_df['cat_encoded'] = train_df['cat_col'].map(mean_target) groups by category, calculates mean target, then maps it back correctly. Other options do not compute mean target per category.

3. Final Answer:

   mean_target = train_df.groupby('cat_col')['target'].mean(); train_df['cat_encoded'] = train_df['cat_col'].map(mean_target) -> Option A

4. Quick Check:

   Group by category and map mean target [OK]

Hint: Group by category and map mean target for encoding [OK]

Common Mistakes:

• Using category codes instead of target mean
• Assigning overall mean target to all rows
• Mapping category length instead of target mean

3. Given the following code, what will be the output of print(test_df['cat_encoded'].tolist())?

import pandas as pd
train_df = pd.DataFrame({'cat_col': ['A', 'B', 'A', 'C'], 'target': [1, 0, 1, 0]})
mean_target = train_df.groupby('cat_col')['target'].mean()
test_df = pd.DataFrame({'cat_col': ['A', 'B', 'C', 'D']})
test_df['cat_encoded'] = test_df['cat_col'].map(mean_target).fillna(0.5)
print(test_df['cat_encoded'].tolist())

medium

A. [1.0, 0.0, 0.0, 0.5]

B. [1.0, 0.0, 0.0, 0.0]

C. [1.0, 0.0, 0.0, NaN]

D. [0.5, 0.5, 0.5, 0.5]

Solution

1. Step 1: Calculate mean target per category from training data

   'A' has targets [1,1] mean=1.0, 'B' has [0] mean=0.0, 'C' has [0] mean=0.0.

2. Step 2: Map test categories and fill missing

   Test categories 'A','B','C' map to 1.0,0.0,0.0 respectively. 'D' is missing, so fillna(0.5) sets it to 0.5.

3. Final Answer:

   [1.0, 0.0, 0.0, 0.5] -> Option A

4. Quick Check:

   Map known means, fill unknown with 0.5 [OK]

Hint: Fill missing categories with default value after mapping [OK]

Common Mistakes:

• Not filling missing categories, resulting in NaN
• Using overall mean instead of per-category mean
• Miscomputing mean target values

4. You applied target encoding on your training data and then directly applied the same encoding on test data using the training means. However, your model shows signs of overfitting. What is the most likely mistake?

medium

A. You replaced missing values with zero instead of the mean

B. You did not normalize the target variable before encoding

C. You used target encoding on the entire dataset before splitting into train and test

D. You used one-hot encoding instead of target encoding

Solution

1. Step 1: Understand overfitting cause in target encoding

   Overfitting often happens if target encoding uses information from the test set or entire data before splitting.

2. Step 2: Identify mistake in data leakage

   Encoding before splitting leaks target info from test data into training, causing overfitting. Other options do not explain this leakage.

3. Final Answer:

   You used target encoding on the entire dataset before splitting into train and test -> Option C

4. Quick Check:

   Encoding before split causes data leakage [OK]

Hint: Always fit encoding only on training data to avoid leakage [OK]

Common Mistakes:

• Encoding before train-test split causing leakage
• Confusing normalization with encoding
• Ignoring missing value handling

5. You have a categorical feature with many rare categories in your training data. How can you apply target encoding to reduce overfitting caused by these rare categories?

hard

A. Use one-hot encoding instead of target encoding for rare categories

B. Use smoothing by combining category mean with overall mean weighted by category frequency

C. Apply target encoding only on the most frequent category and ignore others

D. Replace rare categories with a fixed constant before encoding

Solution

1. Step 1: Understand overfitting from rare categories

   Rare categories have few samples, so their target mean can be noisy and cause overfitting.

2. Step 2: Apply smoothing to reduce noise

   Smoothing blends the category mean with the overall mean, weighted by how many samples the category has, reducing noise for rare categories.

3. Final Answer:

   Use smoothing by combining category mean with overall mean weighted by category frequency -> Option B

4. Quick Check:

   Smoothing balances rare category means with global mean [OK]

Hint: Smooth rare categories by mixing with overall mean [OK]

Common Mistakes:

• Ignoring rare categories causing noisy means
• Replacing rare categories with constants losing info
• Using one-hot encoding which increases dimensionality