BASIC ML

Which of the following statements best describes Machine Learning?

• The process of writing code to perform specific tasks.

• The ability of machines to think and reason like humans.

• The field of study that gives computers the ability to learn without being explicitly programmed.

• The process of automating repetitive tasks using computers.

What is the main goal of supervised learning?

• To classify data into specific categories.

• To uncover hidden patterns or structures in data.

• To make predictions or decisions based on labeled examples.

• To optimize the performance of an algorithm by adjusting its parameters.

Which of the following techniques is used for dimensionality reduction in Machine Learning?

• Clustering

• Regression

• Feature extraction

• Support Vector Machines

What is the purpose of a validation set in Machine Learning?

• To train the model on unseen data to improve its generalization.

• To evaluate the performance of the model on the training data.

• To fine-tune the hyperparameters of the model.

• To test the model's performance on completely new data.

What is the purpose of regularization in Machine Learning?

• To reduce bias in the model's predictions.

• To reduce variance in the model's predictions.

• To improve the interpretability of the model.

• To speed up the training process.

Which of the following techniques is used to handle missing data in Machine Learning?

• Dropping the missing values

• Replacing missing values with the median

• Replacing missing values with the mean

• All of the above

What is the purpose of feature scaling in Machine Learning?

• To convert categorical variables into numerical variables.

• To handle missing values in the dataset.

• To normalize the features to a similar scale, preventing some features from dominating others.

• To reduce the dimensionality of the dataset.

What is the purpose of cross-validation in Machine Learning?

• To assess the performance of the model on unseen data.

• To divide the dataset into training and testing sets.

• To fine-tune the hyperparameters of the model.

• To handle missing values in the dataset.

What is the purpose of the term "overfitting" in Machine Learning?

• To refer to the situation when the model performs well on the training data but poorly on new, unseen data.

• To refer to the situation when the model is unable to capture the underlying patterns in the data.

• To refer to the situation when the model is too simple and fails to capture the complexities in the data.

• To refer to the situation when the model is too complex and captures noise in the data.

What is the purpose of the term "underfitting" in Machine Learning?

• To refer to the situation when the model performs well on the training data but poorly on new, unseen data.

• To refer to the situation when the model is unable to capture the underlying patterns in the data.

• To refer to the situation when the model is too simple and fails to capture the complexities in the data.

• To refer to the situation when the model is too complex and captures noise in the data.