Machine Learning is a subset of artificial intelligence that focuses on building models that can learn from data and make predictions or take actions. Unlike traditional programming, where explicit rules are defined, in Machine Learning, algorithms learn patterns from data to make decisions or predictions.
Supervised learning algorithms learn from labeled data, where the input features are known, and the corresponding output or target variable is provided. Unsupervised learning algorithms, on the other hand, learn from unlabeled data, discovering patterns or structures within the data without specific target variables.
Cross-validation is used to assess the performance of a machine learning model. The cross validation involves in dividing the data into multiple subsets, training the model on some subsets, and evaluating its performance on the remaining subset. It helps to estimate how well the model will generalize to unseen data.
The bias-variance tradeoff refers to the balance between a ability of model to fit the training data and its ability to generalize to new, unseen data (low variance). A model with high bias underfits the data, while a model with high variance overfits the data. Finding the optimal tradeoff is crucial for building a well-performing model.
Overfitting occurs when a model performs well on the training data but fails to generalize to new data. It happens when the model captures noise or irrelevant patterns in the training set. Regularization techniques, such as L1 and L2 regularization, can help prevent overfitting by adding penalties to the model's complexity.
Common evaluation metrics include accuracy, precision, recall, F1 score, and area under the ROC curve (AUC-ROC). The choice of metric depends on the problem type, such as classification or regression, and the specific requirements of the problem at hand.
Feature selection is a process of selecting a subset of relevant features from the original feature set. It is important as it helps to improve model performance, reduce complexity, mitigate overfitting, and enhance interpretability. Techniques such as correlation analysis, recursive feature elimination, and regularization can be used for feature selection.
Classification algorithms are used to predict discrete class categories or labels categories, while regression algorithms are used to predict continuous numerical values. In classification, the output is categorical, while in regression, it is numerical.
The steps include: - Data preprocessing: Cleaning, transforming, and normalizing the data. - Feature selection and engineering: Selecting relevant features and creating new features. - Model selection and training: Choosing a suitable model and training it on the data. - Hyperparameter tuning: Fine-tuning the model's parameters to optimize performance. - Evaluation: Assessing the model's performance using appropriate metrics. - Deployment: Integrating the model into a production environment for inference.
Regularization adds a penalty term to the model's objective function, discouraging the model from learning overly complex patterns from the data. This helps to prevent overfitting by controlling the model