Exploring Artificial Intelligence

What is the primary goal of artificial intelligence?

• To enhance human intelligence through direct brain interfaces.

• To create machines that can only perform simple calculations.

• To develop systems that can replace human workers entirely.

• To create systems that can perform tasks requiring human intelligence.

How do decision trees work in machine learning algorithms?

• Decision trees are used exclusively for regression tasks and not for classification.

• Decision trees require a fixed number of features and cannot adapt to new data.

• Decision trees only work with numerical data and cannot handle categorical features.

• Decision trees classify data by splitting it into subsets based on feature values, creating a tree-like model of decisions.

What is the difference between supervised and unsupervised learning?

• Supervised learning requires no data for training, while unsupervised learning requires all data to be labeled.

• Supervised learning is used for clustering, while unsupervised learning is used for classification.

• Supervised learning can only be applied to images, while unsupervised learning can only be applied to text.

• Supervised learning uses labeled data for training, while unsupervised learning uses unlabeled data to find patterns.

Explain the concept of overfitting in statistical models.

• Overfitting is when a model captures noise in the training data, resulting in poor generalization to new data.

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

• Overfitting occurs when a model is too simple and fails to capture the underlying patterns.

• Overfitting refers to the process of reducing the complexity of a model to improve performance.

What statistical method is commonly used to evaluate the performance of a classification algorithm?

• Principal component analysis

• K-means clustering

• Confusion matrix and related metrics (accuracy, precision, recall, F1 score)

• Linear regression analysis