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Feature and label notation

In ML, features are typically represented as matrix X and labels as vector y.

Feature matrix X

By convention inputs are written as capital X and labels as lowercase y. X is two-dimensional (examples × features), y is one-dimensional (one label per example).

import numpy as np
X = np.array([[170, 65], [160, 55], [180, 80]])  # height, weight
y = np.array([1, 0, 1])
print(X.ndim, y.ndim)   # 2 1

The capital letter for a matrix and the lowercase for a vector come from mathematical notation.

reshape(-1, 1)

The way to turn single-feature data into a column is reshape(-1, 1). -1 means "work out this dimension yourself":

x = np.array([10, 20, 30])
X = x.reshape(-1, 1)
print(X.shape)   # (3, 1)

The labels should stay one-dimensional; turning y into a column causes a warning in some models.

Practice Turn into a column

Turn the array x into X with reshape(-1, 1); print X.shape and X.ndim separated by a space.

Starter code

model.py
import numpy as np

x = np.array([4, 8, 15, 16, 23, 42])

# buraya yaz

Sample solution

model.py
import numpy as np

x = np.array([4, 8, 15, 16, 23, 42])

X = x.reshape(-1, 1)
print(X.shape, X.ndim)

Expected output

(6, 1) 2

Questions and answers

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

1 Why can't <code>X = np.array([10, 20, 30])</code> be given to scikit-learn directly?

  • Three examples are too few
  • NumPy arrays aren't accepted
  • It is one-dimensional; X must be two-dimensional Correct answer
  • The numbers are integers

Explanation: It must be converted to a single-column two-dimensional array with reshape(-1, 1).

2 Turn the one-dimensional array into a single-column matrix.

Question 
X = x.reshape(____)
Answer 
X = x.reshape(-1, 1)

Explanation: reshape(-1, 1) works out the row count automatically and produces a single column.

3 Match the notations.

Term Meaning
X Feature matrix
y Label vector
ndim Number of dimensions
reshape(-1, 1) Turning into one column

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