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What is a feature?

A feature is a measurable data column used as model input.

What is a feature?

A feature is a single measurement the model knows about an example: the house's square metres, the customer's age, the number of links in an e-mail. The model can only learn what it sees in the features.

# house: [square_m, rooms, floor, building_age]
X = np.array([[120, 3, 2, 10],
              [ 85, 2, 5, 25]])

Good features often make more difference than the choice of algorithm. Producing new, meaningful features with domain knowledge is called feature engineering.

Signs of a good feature

A good feature:

  • Is related to the target (square metres to price).
  • Is known at prediction time too: contains no information from the future.
  • Has little noise and is filled in most examples.
# LEAKAGE: putting a "debt_collection_started" column in a credit risk model
# that information only appears once the loan fails; it doesn't exist at prediction time

Features that indirectly contain the target give great results in training and useless ones in reality; this is a kind of data leakage.

Practice Derive a feature

The first column of X is square metres and the second is the room count. Compute square metres per room, add it to X as a new column with np.column_stack, and print the new matrix's shape and its last column.

Starter code

model.py
import numpy as np

X = np.array([[120, 3], [90, 2], [150, 5]], dtype=float)

# buraya yaz

Sample solution

model.py
import numpy as np

X = np.array([[120, 3], [90, 2], [150, 5]], dtype=float)

alan_oda = X[:, 0] / X[:, 1]
X2 = np.column_stack([X, alan_oda])
print(X2.shape)
print(X2[:, -1])

Expected output

(3, 3)
[40. 45. 30.]

Questions and answers

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

1 Which is the most meaningful derived feature for a house price model?

  • Square metres per room Correct answer
  • The row number
  • The owner's name
  • The time of entry

Explanation: Area per room describes how spacious the house is; the others are unrelated to price.

2 Compute the area-per-room feature.

Question 
alan_oda = X[:, 0] ____ X[:, 1]
Answer 
alan_oda = X[:, 0] / X[:, 1]

Explanation: Ratio features capture the relationship of two measurements in one number.

3 Match the features with the assessment.

Term Meaning
square metres Related to price
kayıt_id Meaningless
satış_sonrası_değer Leakage
area per room Derived, useful

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