Target encoding in ML Python - Model Pipeline Trace

Model Pipeline - Target encoding

Target encoding replaces categories in a feature with the average value of the target for those categories. This helps models use categorical data better by turning categories into numbers that relate to the prediction goal.

Data Flow - 4 Stages

1Raw data input

1000 rows x 3 columns→Original dataset with categorical feature and target→1000 rows x 3 columns

Feature: Color = ['Red', 'Blue', 'Green', ...], Target: Sale = [1, 0, 1, ...]

↓

2Calculate target mean per category

1000 rows x 3 columns→Group by categorical feature and compute mean target→Number of unique categories x 2 columns

Color: Red -> mean target 0.7, Blue -> 0.4, Green -> 0.5

↓

3Replace categories with target mean

1000 rows x 3 columns→Map each category to its target mean value→1000 rows x 3 columns

Color: Red replaced by 0.7, Blue by 0.4, Green by 0.5

↓

4Model training

1000 rows x 3 columns→Train model using encoded feature and other features→Trained model

Model learns relationship between encoded color and sale

Training Trace - Epoch by Epoch

Loss
0.7 |****
0.6 |*** 
0.5 |**  
0.4 |*   
0.3 |    
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.6	Model starts learning, loss is high, accuracy low
2	0.5	0.72	Loss decreases, accuracy improves as model learns patterns
3	0.4	0.8	Model continues improving, better fit to data
4	0.35	0.85	Loss decreases further, accuracy rises
5	0.32	0.87	Model converges with good accuracy

Prediction Trace - 3 Layers

Layer 1: Input sample

Layer 2: Target encoding mapping

Layer 3: Model prediction

Model Quiz - 3 Questions

What does target encoding replace in the data?

A Target values with categories

B Categories with their average target value

C Categories with random numbers

D Missing values with zero

Key Insight

Target encoding helps convert categories into meaningful numbers based on the target. This makes it easier for models to find patterns and improve prediction accuracy.

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