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What is classification?

Classification is the task of assigning data to one of predefined classes.

What is classification?

Classification predicts which of a set of predefined classes an example belongs to: is the e-mail spam, is the tumour benign, which digit is in the photo. The output is not a number but a label.

from sklearn.linear_model import LogisticRegression
X = [[1], [2], [3], [7], [8], [9]]
y = ["düşük", "düşük", "düşük", "yüksek", "yüksek", "yüksek"]
m = LogisticRegression().fit(X, y)
print(m.predict([[2.5], [7.5]]))   # ['düşük' 'yüksek']

Labels can be text; scikit-learn keeps the classes sorted in the classes_ attribute.

Logistic regression

Despite its name, logistic regression is a classifier. It squeezes a linear score between 0 and 1 with the sigmoid function and interprets the result as a probability:

def sigmoid(z):
    return 1 / (1 + np.exp(-z))
print(sigmoid(0))    # 0.5   decision boundary
print(sigmoid(4))    # 0.98
print(sigmoid(-4))   # 0.018

If the probability is above 0.5 the positive class is chosen. It is a fast, interpretable and strong baseline model.

Practice Sigmoid

Write the sigmoid(z) function with np.exp. Print the results for np.array([-2, 0, 2]) with np.round(..., 3).

Starter code

model.py
import numpy as np

# buraya yaz

Sample solution

model.py
import numpy as np

def sigmoid(z):
    return 1 / (1 + np.exp(-z))

print(np.round(sigmoid(np.array([-2, 0, 2])), 3))

Expected output

[0.119 0.5   0.881]

Questions and answers

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

1 Which is a classification output?

  • Sales count 340
  • "approved" / "rejected" Correct answer
  • Tomorrow's temperature 21.3
  • House price 1.2 million

Explanation: It is a choice from a limited label set; the others are continuous numbers.

2 Create the logistic regression classifier.

Question 
m = ____()
Answer 
m = LogisticRegression()

Explanation: LogisticRegression lives in sklearn.linear_model and does classification.

3 Match the concepts.

Term Meaning
label Class name
sigmoid Squeezes into 0-1
0.5 Default decision threshold
classes_ Sorted classes

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