Why classification predicts categories in ML Python - Challenge Your Understanding

Challenge - 5 Problems

🧠 Conceptual

intermediate

Why does classification output categories?

Imagine you have a basket of fruits and you want to sort them into apples, oranges, and bananas. Why does a classification model predict categories instead of numbers?

A Because classification models assign each input to a specific group or label representing a category.

B Because classification models calculate the average value of the input features.

C Because classification models predict continuous values like weight or price.

D Because classification models generate random numbers for each input.

❓ Predict Output

intermediate

Output of classification prediction code

What is the output of this code that uses a simple classification model to predict the category of a flower?

ML Python

from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier

iris = load_iris()
X, y = iris.data, iris.target
model = DecisionTreeClassifier(random_state=0)
model.fit(X, y)
prediction = model.predict([[5.1, 3.5, 1.4, 0.2]])
print(prediction)

A [3]

B [0]

C [1]

D [2]

❓ Model Choice

advanced

Choosing a model for category prediction

You want to build a model that predicts if an email is spam or not (two categories). Which model is best suited for this classification task?

A Principal Component Analysis (PCA)

B K-Means Clustering

C Linear Regression

D Logistic Regression

❓ Metrics

advanced

Which metric measures classification accuracy?

You trained a classification model and want to know how often it predicts the correct category. Which metric should you use?

A Accuracy

B Mean Squared Error (MSE)

C R-squared

D Root Mean Squared Error (RMSE)

🔧 Debug

expert

Why does this classification code raise an error?

Consider this code snippet that tries to predict categories but raises an error. What is the cause?

ML Python

from sklearn.tree import DecisionTreeClassifier

model = DecisionTreeClassifier()
model.fit([[1, 2], [3, 4]], [0, 1])
prediction = model.predict([1, 2])
print(prediction)

A The model was not trained before prediction.

B DecisionTreeClassifier cannot handle numeric inputs.

C The input to predict should be a 2D array, but [1, 2] is 1D.

D The labels should be strings, not integers.