"Supervised learning (SL) is a paradigm in machine learning where input objects and a desired output value train a model."
Learning from labelled data, including classification, regression, and decision trees.
Regression: A supervised learning approach where the goal is to predict continuous values.
Classification: A supervised learning approach where the goal is to categorize input into one of several possible classes.
Decision Trees: A tree-like model where internal nodes represent tests on input attributes, and leaves represent possible classifications.
Random Forest: A supervised learning method that constructs multiple decision trees and combines their predictions.
Naive Bayes: A probabilistic classifier that assumes the features are independent and calculates the probability of each class for a given input.
Support Vector Machines (SVM): A supervised learning method that tries to maximize the margin between the decision boundary and the training data.
Artificial Neural Networks: A set of algorithms that attempt to recognize patterns in data based on input-output training examples.
Gradient Boosting: An approach that builds an ensemble of weak learners by sequentially adding new ones that focus on harder-to-predict instances.
Principal Component Analysis (PCA): A technique used to reduce the dimensionality of large datasets while preserving as much variance as possible.
Overfitting and underfitting: Common issues in supervised learning where a model either memorizes the training data or fails to generalize well to unseen data.
Model selection and hyperparameter tuning: Techniques for choosing the best model and adjusting its parameters to optimize performance on a specific problem.
Cross-validation: A method for estimating the generalization ability of a model by testing it on multiple subsets of the training data.
Regularization: A technique used to prevent overfitting by adding a penalty term to the objective function during training.
Transfer learning: A machine learning approach where a model trained on one task is used to improve performance on a related but different task.
Ensemble learning: A technique where multiple models are trained and combined to improve overall performance.
Regression: Regression is a type of Supervised Learning in which the goal is to predict a continuous value output by applying a mathematical function to the input features. Example: Predicting house prices based on square footage, location, and other factors.
Classification: Classification is a type of Supervised Learning in which the goal is to predict a discrete output value or a class label for each input sample. Example: Classifying emails as spam or not spam.
Binary classification: Binary classification is a type of Supervised Learning in which the output classification is either True or False, 1 or 0, Yes or No, etc. Example: Predicting whether a customer will buy a product or not.
Multiclass classification: Multiclass classification is a type of Supervised Learning in which the output classification can have more than two possible values or class labels. Example: Classifying flowers into different species based on their petal width, length, sepal width, etc.
Ordinal classification: In Ordinal classification, the output classification has a specific order, and it is not just a label. Example: Classifying hotel rooms into categories like deluxe, standard, and suite based on their facilities.
Imbalanced classification: Imbalanced classification is a type of Supervised Learning in which the input dataset has an uneven distribution of class labels. Example: Fraud detection in bank transactions, where the number of fraud cases is much less than non-fraud cases.
Sequence prediction: Sequence prediction is a type of Supervised Learning in which the goal is to predict a sequence of output values based on a sequence of input values. Example: Predicting the weather for the next week based on past weather patterns.
Time-series prediction: Time-series prediction is a type of Supervised Learning in which the goal is to predict the future behavior of a dynamic system based on time-series data. Example: Predicting stock prices based on past stock prices.
Structured output: Structured output is a type of Supervised Learning in which the output is not just a single value but a complex structured object, such as a tree, graph or sentence. Example: Predicting the structure of a protein molecule based on its chemical composition.
Ensemble learning: Ensemble learning is a type of Supervised Learning in which multiple models are combined to make a single final prediction. Example: Random forest model.
"Input objects are, for example, a vector of predictor variables."
"The desired output value is also known as a human-labeled supervisory signal."
"The training data is processed, building a function that maps new data on expected output values."
"The goal is for the algorithm to correctly determine output values for unseen instances."
"The learning algorithm needs to generalize from the training data to unseen situations in a 'reasonable' way."
"Inductive bias is essential for the learning algorithm to generalize from the training data to unseen situations."
"It is measured through the so-called generalization error."
"The generalization error determines how well an algorithm performs on unseen instances."
"An optimal scenario allows the algorithm to correctly determine output values for unseen instances."
"A function is built during the training process to map new data onto expected output values."
"Input objects can be a vector of predictor variables."
"Supervised learning is a specific paradigm where input objects and a desired output value are used to train a model."
"The training data is processed to build a function that maps new data onto expected output values."
"The learning algorithm needs to generalize from the training data to unseen situations."
"It is important for the algorithm to generalize to unseen instances in a 'reasonable' way."
"The performance is measured through the generalization error."
"The statistical quality is determined by how well the algorithm generalizes from training data to unseen situations."
"They aim to accurately determine output values for unseen instances."
"The desired output values, known as human-labeled supervisory signals, help train the model in supervised learning."