Prediction
- Machine Learning course
- Beginner
- Lesson 73/112
- 7 min
Make predictions with the model.
Prediction and probability
The trained pipeline takes new examples directly with raw measurements. predict_proba shows how confident the decision is:
p = model.predict(X_test[:5])
print(p)
print(model.predict_proba(X_test[:2]).round(2))In KNN the probabilities are the shares of neighbour votes; with 5 neighbours the values are multiples of 0.2.
Finding wrong predictions
With a boolean mask the indices of wrongly predicted examples are found; then by examining those examples' probabilities you learn where the model is undecided:
tahmin = model.predict(X_test)
yanlis = np.where(tahmin != y_test)[0]
print(yanlis)
print(model.predict_proba(X_test[yanlis]).round(2))Most errors are usually between two neighbouring classes; the confusion matrix confirms this.
Practice Inspect the errors
Get the predictions for the test set. Print the number of wrong predictions, then the true classes of the wrongly predicted examples (y_test[yanlis]).
Starter code
import numpy as np
from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
X, y = load_wine(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42, stratify=y)
model = make_pipeline(StandardScaler(), KNeighborsClassifier(n_neighbors=5)).fit(X_train, y_train)
# buraya yazSample solution
import numpy as np
from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
X, y = load_wine(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42, stratify=y)
model = make_pipeline(StandardScaler(), KNeighborsClassifier(n_neighbors=5)).fit(X_train, y_train)
tahmin = model.predict(X_test)
yanlis = np.where(tahmin != y_test)[0]
print(len(yanlis))
print(y_test[yanlis])Expected output
3 [1 2 1]
Questions and answers
Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.
1 With a 5-neighbour KNN, which can't be a class's probability?
- 0.5 Correct answer
- 0.4
- 0.6
- 1.0
Explanation: The probability has the form k/5: 0, 0.2, 0.4, 0.6, 0.8, 1.0.
2 Get the indices of the wrong predictions.
yanlis = np.where(tahmin ____ y_test)[0]yanlis = np.where(tahmin != y_test)[0]Explanation: != produces an element-wise inequality mask.
3 Match the expressions.
| Term | Meaning |
|---|---|
model.predict |
Class decision |
model.predict_proba |
Neighbour vote shares |
tahmin != y_test |
Error mask |
np.where(...)[0] |
Error indices |