CodeBlanks Logo Icon CodeBlanks
Login Register

Categorical features

Categorical features are converted to binary vectors with OneHotEncoder.

Categorical features

Text columns with a limited set of values, like city, colour or product type, are categorical. Unordered categories are encoded with one-hot, ordered ones (small < medium < large) with OrdinalEncoder:

from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder
sehir = [["Ankara"], ["İzmir"], ["Ankara"]]
print(OneHotEncoder().fit_transform(sehir).toarray())
boy = [["küçük"], ["büyük"], ["orta"]]
oe = OrdinalEncoder(categories=[["küçük", "orta", "büyük"]])
print(oe.fit_transform(boy).ravel())   # [0. 2. 1.]

Without categories, OrdinalEncoder uses alphabetical order, which is often not the meaningful order.

Unknown categories and rare values

New categories are inevitable in production; handle_unknown="ignore" handles them safely. If there are many rare categories, min_frequency groups them into a single "infrequent" column:

enc = OneHotEncoder(handle_unknown="ignore")
enc.fit([["Ankara"], ["İzmir"]])
print(enc.transform([["Bursa"]]).toarray())   # [[0. 0.]]

For columns with thousands of distinct values (like product codes), one-hot produces very wide matrices; methods like target encoding are considered then.

Practice Ordinal encode

Encode the egitim column with OrdinalEncoder(categories=[["lise", "lisans", "doktora"]]) and print the result with ravel().

Starter code

model.py
from sklearn.preprocessing import OrdinalEncoder

egitim = [["lisans"], ["lise"], ["doktora"], ["lisans"]]

# buraya yaz

Sample solution

model.py
from sklearn.preprocessing import OrdinalEncoder

egitim = [["lisans"], ["lise"], ["doktora"], ["lisans"]]

oe = OrdinalEncoder(categories=[["lise", "lisans", "doktora"]])
print(oe.fit_transform(egitim).ravel())

Expected output

[1. 0. 2. 1.]

Questions and answers

Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.

1 If a city not seen in training appears in the test data, what does OneHotEncoder do by default?

  • It uses the most frequent city
  • It errors Correct answer
  • It opens a new column
  • It uses zeros

Explanation: With handle_unknown="ignore", the unknown value becomes an all-zero row.

2 Handle unknown categories without errors.

Question 
enc = OneHotEncoder(handle_unknown=____)
Answer 
enc = OneHotEncoder(handle_unknown="ignore")

Explanation: "ignore" turns an unknown value into a zero vector.

3 Match the encodings.

Term Meaning
Color One-hot
Size S/M/L Ordinal encoding
New city handle_unknown
Rare categories min_frequency

All lessons in Beginner

1 What is ML?

  1. What is machine learning?
  2. Supervised and unsupervised
  3. Data and model
  4. Training and testing
  5. Feature and label notation
  6. Summary - ML fundamentals

2 NumPy basics

  1. What is NumPy?
  2. Creating arrays
  3. Array properties
  4. Array indexing
  5. Reshaping arrays
  6. Summary - NumPy basics

3 Pandas basics

  1. What is Pandas?
  2. Creating a DataFrame
  3. Column operations
  4. Selecting and filtering data
  5. Viewing first rows
  6. Summary - Pandas basics

4 Data preprocessing

  1. Data cleaning
  2. Missing data
  3. Data types
  4. Label encoding
  5. Data splitting
  6. Summary - Data preprocessing

5 Statistics basics

  1. Mean
  2. Median
  3. Standard deviation
  4. Variance
  5. Correlation
  6. Summary - Statistics

6 Linear algebra

  1. Vectors
  2. Matrices
  3. Matrix multiplication
  4. Transpose
  5. Identity matrix
  6. Summary - Linear algebra

7 Project: Data analysis

  1. Loading data
  2. Cleaning data
  3. Computing statistics
  4. Printing results

8 Regression

  1. What is regression?
  2. Linear regression
  3. Model training
  4. Prediction
  5. Error measurement
  6. Summary - Regression

9 KNN

  1. What is KNN?
  2. The K parameter
  3. Distance measurement
  4. Classification
  5. Model training
  6. Summary - KNN

10 Decision trees

  1. What is a decision tree?
  2. Splitting criterion
  3. Depth
  4. Classification
  5. Overfitting
  6. Summary - Decision trees

11 Classification

  1. What is classification?
  2. Binary classification
  3. Multi-class
  4. Accuracy
  5. Confusion matrix
  6. Summary - Classification

12 Model evaluation

  1. Train-test split
  2. Accuracy
  3. Precision
  4. Recall
  5. F1 score
  6. Summary - Model evaluation

13 Project: Classification

  1. Preparing data
  2. Training model
  3. Prediction
  4. Evaluation

14 Scaling

  1. What is scaling?
  2. StandardScaler
  3. MinMaxScaler
  4. When to use?
  5. Pipeline
  6. Summary - Scaling

15 Feature engineering

  1. What is a feature?
  2. Feature selection
  3. Feature extraction
  4. Categorical features
  5. Numerical features
  6. Summary - Feature engineering

16 Model selection

  1. What is model selection?
  2. Cross-validation
  3. Grid search
  4. Score comparison
  5. Hyperparameter
  6. Summary - Model selection

17 Project: Regression

  1. Preparing data
  2. Training model
  3. Prediction
  4. Error measurement

18 Metrics

  1. What is a metric?
  2. Accuracy
  3. Precision
  4. Recall
  5. F1 score
  6. Summary - Metrics

19 Data visualization

  1. What is visualization?
  2. Scatter plot
  3. Histogram
  4. Line plot
  5. Box plot
  6. Summary - Visualization

20 Beginner final

  1. Challenge 1
  2. Challenge 2
  3. Challenge 3
  4. Challenge 4