Exploring Machine Learning Concepts

What is the primary goal of machine learning?

• To create static algorithms that do not adapt.

• To store data without analysis.

• To replace human intelligence entirely.

• To enable computers to learn from data and make predictions or decisions.

Define supervised learning and provide an example.

• Unsupervised learning involves training on unlabelled data.

• Supervised learning is only applicable to image recognition tasks.

• Supervised learning is a type of machine learning where a model is trained on labeled data. An example is classifying emails as 'spam' or 'not spam'.

• An example of supervised learning is clustering data points into groups.

What is the difference between classification and regression?

• Classification requires more data than regression.

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

• Classification predicts numerical values; regression predicts categories.

• Classification predicts categories; regression predicts continuous values.

Explain the concept of overfitting in machine learning.

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

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

• Overfitting is when a model learns the training data too well, leading to poor performance on new data.

• Overfitting happens when a model is trained on too little data, leading to generalization issues.

What role does a training set play in machine learning?

• The training set is a collection of random data unrelated to the model's task.

• The training set is used to evaluate the performance of a model.

• The training set is used to train a machine learning model by providing it with examples to learn from.

• The training set is only for testing the model after it has been trained.

Describe the purpose of a confusion matrix.

• To summarize the data distribution in a dataset.

• To calculate the accuracy of a regression model.

• The purpose of a confusion matrix is to provide a visual representation of the performance of a classification model by comparing predicted and actual outcomes.

• To visualize the relationship between features in a dataset.

What is the significance of feature selection?

• Feature selection improves model performance and interpretability by identifying the most relevant features.

• Feature selection has no impact on model accuracy.

• Feature selection is only relevant for linear models.

• Feature selection increases the number of features used in a model.

Name two popular machine learning algorithms.

• K-Means Clustering

• Support Vector Machines

• Naive Bayes

• Linear Regression, Decision Trees

How does unsupervised learning differ from supervised learning?

• Unsupervised learning differs from supervised learning in that it uses unlabeled data to identify patterns, while supervised learning uses labeled data to predict outcomes.

• Supervised learning uses only unlabeled data for predictions.

• Unsupervised learning requires labeled data to identify patterns.

• Unsupervised learning is a type of supervised learning.

What is the importance of cross-validation in model evaluation?

• Cross-validation is only useful for small datasets.

• Cross-validation guarantees the best model will be selected.

• Cross-validation is primarily used for data preprocessing.

• Cross-validation is important for evaluating model performance and ensuring generalization to new data.