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Prediction

Make predictions with the model.

Prediction

With the chosen model we predict for the patients in the test set. Seeing predictions next to the true values shows in which range the model struggles:

p = model.predict(X_test)
for gercek, tahmin in zip(y_test[:5], p[:5]):
    print(f"{gercek:6.0f} {tahmin:8.1f}")

Linear models tend to pull extreme values towards the mean: they predict very high progression lower than it is.

Examining residuals

The mean of the residuals should be close to 0 and show no clear pattern. The largest absolute residuals are the patients the model got most wrong:

kalinti = y_test - p
print(kalinti.mean().round(1))
en_kotu = np.argsort(np.abs(kalinti))[-3:]
print(y_test[en_kotu], p[en_kotu].round(0))

np.argsort gives the indices sorted from smallest to largest; the last three are the biggest errors.

Practice Prediction and residual

Get the test predictions with model. Print the first 3 predictions with np.round(..., 1), then the largest absolute residual with round(..., 1).

Starter code

model.py
import numpy as np
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split

X, y = load_diabetes(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
from sklearn.linear_model import LinearRegression
model = LinearRegression().fit(X_train, y_train)

# buraya yaz

Sample solution

model.py
import numpy as np
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split

X, y = load_diabetes(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
from sklearn.linear_model import LinearRegression
model = LinearRegression().fit(X_train, y_train)

p = model.predict(X_test)
print(np.round(p[:3], 1))
kalinti = y_test - p
print(round(np.abs(kalinti).max(), 1))

Expected output

[238.5 248.9 164.1]
162.7

Questions and answers

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

1 What is a residual?

  • The size of the test set
  • The true value minus the prediction Correct answer
  • The mean of the predictions
  • A model weight

Explanation: Residual = y - ŷ; if positive the model under-predicted, if negative it over-predicted.

2 Get the indices sorting the absolute residuals from smallest to largest.

Question 
sira = np.____(np.abs(kalinti))
Answer 
sira = np.argsort(np.abs(kalinti))

Explanation: argsort returns indices, not values.

3 Match the expressions.

Term Meaning
y_test - p Residuals
np.abs(kalinti) Absolute error
np.argsort(...)[-3:] Three largest errors
kalinti.mean() Systematic bias

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