Machine Learning Flashcards

What is the primary goal of machine learning?

Answer: To make predictions or decisions without being explicitly programmed

The primary goal of machine learning is to enable computer systems to learn from data and make predictions or decisions without being explicitly programmed for every scenario. Instead of following fixed instructions, ML algorithms build models based on training data, allowing them to identify patterns and adapt their behavior. This capability is fundamental to developing intelligent systems that can automate complex tasks and derive insights.

Which of the following is a supervised learning algorithm?

Answer: Decision Trees

Decision Trees are a classic example of a supervised learning algorithm. In supervised learning, the algorithm learns from a labeled dataset, meaning each input data point is paired with its correct output label. Decision Trees use this labeled data to create a model that can predict outcomes for new, unseen data, making them suitable for both classification and regression tasks.

In machine learning, what is "overfitting"?

Answer: When a model performs well on training data but poorly on new data

Overfitting occurs in machine learning when a model learns the training data too well, including its noise and specific quirks, rather than the underlying general relationships. This results in the model performing exceptionally well on the data it was trained on but poorly on new, unseen data. It indicates a failure to generalize effectively, making the model unreliable for real-world predictions.

What is the purpose of cross-validation in machine learning?

Answer: To evaluate a model's performance on unseen data

Cross-validation is a crucial technique used to assess how well a machine learning model will generalize to an independent dataset. It involves partitioning the data into multiple subsets, training the model on some subsets, and testing it on others. This process provides a more robust estimate of the model's performance on unseen data and helps detect issues like overfitting, ensuring the model's reliability.

Which type of machine learning algorithm is commonly used for clustering?

Answer: K-Means

K-Means is a widely used unsupervised learning algorithm specifically designed for clustering tasks. It works by partitioning a dataset into a predefined number of 'K' distinct clusters, where each data point is assigned to the cluster whose mean (centroid) is closest. This algorithm is effective for grouping similar data points together without requiring prior knowledge of their labels.