Fundamentals of Machine Learning

Which of the following is the most accurate definition of data science?

• Data science is performing experiments and recording the data produced by those experiments

• Data science is using computers to analyse data and to perform calculations on the data to produce information

• Data science is extracting meaning from large data sets in order to provide insights to support decision-making

• Data science is writing code to make sure that any inaccuracies in data sets are spotted and removed (cleaned)

What is the primary goal of supervised learning?

• To predict future outcomes without any labeled data.

• To learn a mapping from input features to output labels using labeled data.

• To cluster similar data points into groups.

• To reduce the dimensionality of input features.

Define overfitting in the context of machine learning.

• Overfitting refers to a model that is trained on too little data, leading to poor performance.

• Overfitting is when a model performs equally well on both training and unseen data.

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

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

What is the difference between classification and regression?

• Classification predicts categories; regression predicts continuous values.

• Classification is used for time series; regression is for image analysis.

• Classification predicts numerical values; regression predicts categories.

• Classification requires more data than regression.

Name one common algorithm used for classification tasks.

• K-Means Clustering

• Linear Regression

• Decision Tree

• Monte-Carlo

What does the term 'feature' refer to in machine learning?

• A random selection of data points for training.

• A type of algorithm used in machine learning.

• A measurable property or characteristic used as input for a machine learning model.

• A specific model architecture for neural networks.

Explain the concept of training and testing datasets.

• Training datasets are for model training; testing datasets are for model evaluation.

• Training datasets are for model evaluation; testing datasets are for model training.

• Training datasets are used for data storage; testing datasets are for data collection.

• Training datasets are only for supervised learning; testing datasets are only for unsupervised learning.

What is cross-validation and why is it used?

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

• Cross-validation is used to evaluate the performance of a model and to prevent overfitting.

• Cross-validation is used to improve the speed of model training.

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

Below which are the machine learning technique?

• Supervised Learning

• AI

• Unsupervised Learning

• Reinforcement Learning

How does overfitting affect a machine learning model's performance?

• It improves the model's ability to generalize to new data.

• It causes the model to perform well on training data but poorly on unseen data.

• It has no impact on the model's performance.

• It helps in reducing the model's complexity.