- What is Overfitting in Machine Learning?
Overfitting in machine learning occurs when a model is trained too well on the training data, leading to poor generalization to new, unseen data. This happens when the model learns the noise or random fluctuations in the training data instead of the underlying relationship between the features and the target variable.
- What is Cross-Validation in Machine Learning?
Cross-Validation is a technique used in machine learning to assess the performance of a model on new, unseen data. In cross-validation, the original data is divided into multiple smaller sets, and the model is trained on some of these sets and evaluated on others. This process is repeated several times to get a better estimate of the model’s performance on unseen data.
- What is Feature Scaling in Machine Learning?
Feature scaling in machine learning is the process of transforming the values of the features in the data so that they have similar ranges. This is important because some machine learning algorithms, such as KNN and SVM, are sensitive to the scale of the features and perform poorly when the features are not scaled. Common techniques for feature scaling include normalization and standardization.
- What is the Bias-Variance Tradeoff in Machine Learning?
The bias-variance tradeoff in machine learning refers to the tradeoff between a model’s ability to fit the training data well (low bias) and its ability to generalize to new, unseen data (low variance). Models with high bias are said to be underfitting the training data, while models with high variance are said to be overfitting the training data. Balancing this tradeoff is an important goal in model selection and hyperparameter tuning.
- What is Gradient Descent in Machine Learning?
Gradient descent is an optimization algorithm used in machine learning to find the minimum of a cost function. The algorithm starts with an initial guess for the model parameters and iteratively updates the parameters in the direction of the negative gradient of the cost function, until a minimum is reached. The gradient descent algorithm is used in many machine learning algorithms, including linear regression and neural networks.