CodeBlanks Logo Icon CodeBlanks
Login Register

Summary - Classification

In this group you learned classification, accuracy, and confusion matrix.

Summary: classification

In this group:

  • Classification predicts labels; logistic regression produces probabilities with sigmoid.
  • In a binary problem predict_proba[:, 1] is the positive probability; 0.5 is only the default threshold.
  • With many classes each probability row sums to 1; OvR extends binary models.
  • Accuracy misleads on imbalanced data; DummyClassifier gives a baseline.
  • The confusion matrix separates TP, TN, FP and FN errors.

Practice Classification report

Split Iris with test_size=0.3, random_state=0, stratify=y and train a LogisticRegression(max_iter=1000). Print the test accuracy with round(..., 3), then the confusion matrix.

Starter code

model.py
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix

X, y = load_iris(return_X_y=True)

# buraya yaz

Sample solution

model.py
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix

X, y = load_iris(return_X_y=True)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0, stratify=y)
m = LogisticRegression(max_iter=1000).fit(X_train, y_train)
p = m.predict(X_test)
print(round(accuracy_score(y_test, p), 3))
print(confusion_matrix(y_test, p))

Expected output

1.0
[[15  0  0]
 [ 0 15  0]
 [ 0  0 15]]

Questions and answers

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

1 What should the first comparison be to show that a model is worthwhile?

  • A baseline that predicts the most frequent class Correct answer
  • Training accuracy
  • Another dataset
  • No comparison is needed

Explanation: The model must clearly beat a baseline that learns nothing.

2 Compute the confusion matrix.

Question 
cm = ____(y_test, y_pred)
Answer 
cm = confusion_matrix(y_test, y_pred)

Explanation: confusion_matrix rows are true classes, columns predicted ones.

3 Match the tools.

Term Meaning
LogisticRegression Probabilistic classifier
DummyClassifier Baseline
accuracy_score Share correct
confusion_matrix Error breakdown

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