Python for Data Science and Machine Learning

1.		Which of the following is not a step in the machine learning process?

Data cleaning and preprocessing

Model selection and training

Hyperparameter tuning

Feature engineering

2.		Which of the following is a way to prevent overfitting in machine learning?

Increasing model complexity

Decreasing the amount of training data

Adding more features to the model

Regularization

3.		Which of the following is a method for selecting the optimal hyperparameters in machine learning?

Grid search

Gradient descent

Lasso regularization

None of the above

4.		Which of the following is an unsupervised learning algorithm?

K-means clustering

Naive Bayes

Linear regression

Decision tree

5.		Which library in Python is used for machine learning?

Pandas

NumPy

Scikit-learn

TensorFlow

6.		Which of the following is a hyperparameter in machine learning?

Number of features

Learning rate

Number of samples

Target variable

7.		Which of the following is a regression algorithm in machine learning?

K-means clustering

Decision tree

Random forest

Linear regression

8.		Which of the following is a supervised learning algorithm?

K-means clustering

Random forest

Gradient descent

None of the above

9.		Which of the following is a method for selecting the optimal number of clusters in K-means clustering?

Elbow method

Gradient descent

PCA

None of the above

10.		What is the purpose of the train-test split function in machine learning?

To split the data into training and test sets

To perform cross-validation on the data

To perform hyperparameter tuning on the model

None of the above