NumPy with machine learning libraries - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of NumPy array shape after sklearn train_test_split

What is the shape of X_train after running the following code?

NumPy

import numpy as np
from sklearn.model_selection import train_test_split

X = np.arange(20).reshape(10, 2)
y = np.arange(10)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
print(X_train.shape)

A (7, 2)

B (3, 2)

C (10, 2)

D (14, 2)

💡 Hint

Remember test_size=0.3 means 30% data is for testing, rest for training.

The original data has 10 samples. 30% of 10 is 3 samples for testing, so training has 7 samples. Each sample has 2 features, so shape is (7, 2).

❓ data_output

intermediate

Result of NumPy array after StandardScaler transform

What is the output array after applying StandardScaler to the data below?

NumPy

import numpy as np
from sklearn.preprocessing import StandardScaler

data = np.array([[1, 2], [3, 4], [5, 6]])
scaler = StandardScaler()
scaled_data = scaler.fit_transform(data)
print(np.round(scaled_data, 2))

A

[[1. 1.]
 [3. 4.]
 [5. 6.]]

B

[[-1.22 -1.22]
 [ 0.   0.  ]
 [ 1.22  1.22]]

C

[[0. 0.]
 [0. 0.]
 [0. 0.]]

D

[[-1.  -1.]
 [ 0.   0.]
 [ 1.   1.]]

💡 Hint

StandardScaler centers data to mean 0 and scales to unit variance.

StandardScaler subtracts mean and divides by standard deviation for each feature. The rounded output matches option B.

🔧 Debug

advanced

Identify the error when using NumPy array with sklearn LinearRegression

What error will this code raise when fitting the model?

NumPy

import numpy as np
from sklearn.linear_model import LinearRegression

X = np.array([1, 2, 3, 4, 5])
y = np.array([2, 4, 6, 8, 10])
model = LinearRegression()
model.fit(X, y)

A ValueError: Expected 2D array, got 1D array instead

B TypeError: unsupported operand type(s) for +: 'int' and 'str'

C No error, model fits successfully

D IndexError: index out of bounds

💡 Hint

Check the shape of X passed to fit method.

sklearn expects X to be 2D (samples, features). Passing 1D array causes ValueError.

❓ visualization

advanced

Interpret the plot of PCA components from NumPy data

Given the PCA plot below, which statement is true about the data?

NumPy

import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA

np.random.seed(0)
data = np.dot(np.random.rand(2, 2), np.random.randn(2, 200)).T
pca = PCA(n_components=2)
components = pca.fit_transform(data)
plt.scatter(components[:, 0], components[:, 1])
plt.xlabel('PC1')
plt.ylabel('PC2')
plt.title('PCA of Data')
plt.show()

A PCA components are identical to original features.

B The data has no variance along the first principal component.

C The first principal component explains more variance than the second.

D The second principal component explains more variance than the first.

💡 Hint

PCA orders components by explained variance descending.

PCA sorts components by variance explained. The first component always explains the most variance.

🧠 Conceptual

expert

Why use NumPy arrays with scikit-learn instead of Python lists?

Which is the main reason scikit-learn prefers NumPy arrays over Python lists for input data?

A NumPy arrays automatically normalize data before training.

B Python lists cannot store numerical data.

C Python lists are immutable and cannot be changed.

D NumPy arrays provide efficient memory usage and fast numerical operations required by scikit-learn.

💡 Hint

Think about performance and data structure requirements.

NumPy arrays are optimized for numerical computations and use less memory, which helps scikit-learn run faster.