Python for Data Science and Machine Learning

1.		Which of the following is a method for reducing dimensionality in machine learning?

PCA

K-means clustering

Gradient descent

None of the above

2.		Which of the following is a method for reducing overfitting in machine learning?

Regularization

Gradient descent

Lasso regression

None of the above

3.		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

4.		Which of the following is a method for handling categorical data in machine learning?

One-hot encoding

PCA

Standardization

Gradient descent

5.		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

6.		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

7.		Which of the following is a data preprocessing technique in machine learning?

Standardization

PCA

K-means clustering

Gradient descent

8.		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

9.		What is the purpose of cross-validation in machine learning?

To test the performance of the model on unseen data

To create a validation set for hyperparameter tuning

To reduce overfitting of the model

All of the above

10.		Which of the following is a classification algorithm in machine learning?

Linear regression

K-nearest neighbors

Gradient descent

None of the above