Q&A
Fundamental Concepts
What is the difference between supervised and unsupervised learning?
Supervised learning involves training a model using labeled data, where the desired output is known. The model learns to predict the output from the input data. Examples include classification and regression tasks. Unsupervised learning deals with unlabeled data; the model tries to find inherent patterns or groupings within the data, such as clustering or association rules.
Can you explain the bias-variance tradeoff?
The bias-variance tradeoff refers to the balance between a model's complexity and its performance on training and unseen data. High bias models are simple and may underfit the data, failing to capture underlying patterns. High variance models are complex and may overfit, capturing noise as if it were a pattern. The goal is to find a sweet spot with optimal bias and variance to generalize well.
What is overfitting, and how can you prevent it?
Overfitting occurs when a model learns the training data too well, including its noise and outliers, and performs poorly on new data. It can be prevented by using techniques like cross-validation, simplifying the model, using regularization methods (like L1 or L2 regularization), and ensuring you have enough training data.
Explain the concept of cross-validation.
Cross-validation is a technique for assessing how a predictive model will perform on an independent dataset. It involves partitioning the data into a set of folds, training the model on all but one fold, and testing on the remaining fold. This process is repeated with each fold serving as the test set once. It helps in mitigating overfitting and provides insight into the model's ability to generalize.
What is the central limit theorem, and why is it important?
The central limit theorem states that the sampling distribution of the sample mean approaches a normal distribution as the sample size grows, regardless of the population's distribution, provided the samples are independent and identically distributed. It's important because it allows us to make inferences about population parameters using normal distribution, facilitating hypothesis testing and confidence intervals.