Machine Learning CA-1

What is machine learning?

• Machine learning is a programming language.

• Machine learning is a type of computer hardware.

• Machine learning is a method of data analysis that automates analytical model building.

• Machine learning is a form of traditional statistical analysis.

What is the purpose of data preprocessing in machine learning?

• The purpose of data preprocessing in machine learning is to prepare and clean the data for better model performance.

• To visualize data trends before analysis.

• To increase the size of the dataset.

• To create complex algorithms for predictions.

Define feature engineering and its importance.

• Feature engineering involves using pre-trained models without any data processing.

• Feature engineering is the process of creating and selecting features from raw data to improve machine learning model performance.

• Feature engineering is the process of collecting data without any modifications.

• Feature engineering is only relevant for deep learning models.

What are the differences between training, test, and validation sets?

• Training set: used for final evaluation; Validation set: used for training; Test set: used for tuning.

• Training set: used for tuning; Validation set: used for final evaluation; Test set: used for training.

• Training set: used for training the model; Validation set: used for tuning and validation; Test set: used for final evaluation.

• Training set: used for validation; Validation set: used for final evaluation; Test set: used for training.

Explain the concept of cross-validation.

• Cross-validation is used to eliminate outliers from data.

• Cross-validation is a technique for visualizing data distributions.

• Cross-validation is a method for increasing the size of a dataset.

• Cross-validation is a technique for assessing how the results of a statistical analysis will generalize to an independent data set.

What is k-fold cross-validation?

• A technique for visualizing data distributions in machine learning.

• K-fold cross-validation is a method for evaluating a model's performance by dividing the dataset into 'k' subsets and training/testing the model 'k' times.

• A method for optimizing hyperparameters by using a single training set.

• A process for combining multiple models into one for better accuracy.

What does overfitting mean in the context of machine learning?

• Overfitting refers to a model that generalizes well to all types of data.

• Overfitting occurs when a model is too simple and cannot capture the underlying patterns.

• Overfitting means a model performs well on unseen data but poorly on training data.

• Overfitting means a model performs well on training data but poorly on unseen data due to excessive complexity.

How can underfitting affect a model's performance?

• Underfitting enhances the model's ability to generalize to new data.

• Underfitting improves model performance by reducing bias.

• Underfitting can lead to poor model performance due to high bias and inability to capture data patterns.

• Underfitting allows the model to perfectly fit the training data.

Describe the bias-variance trade-off.

• The bias-variance trade-off refers to the trade-off between speed and accuracy in algorithms.

• The bias-variance trade-off is the relationship between training and testing data.

• The bias-variance trade-off is the difference between supervised and unsupervised learning.

• The bias-variance trade-off is the balance between bias (error from overly simplistic models) and variance (error from overly complex models) in machine learning.

What is a confusion matrix?

• A confusion matrix is a method for data normalization.

• A confusion matrix is a table that displays the performance of a classification model by comparing predicted and actual values.

• A confusion matrix is a type of neural network architecture.

• A confusion matrix is a graphical representation of data distribution.