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What are Supervised Learning Techniques?
Supervised learning techniques are a class of machine learning algorithms that learn from labeled training data to make predictions or classify new, unseen data points. In supervised learning, the algorithm is provided with input-output pairs, where the input data is accompanied by corresponding labels or target values.
Below are some commonly used supervised learning techniques:
Regression: Regression algorithms are used for predicting continuous numeric values. The algorithm learns the relationship between the input variables and the continuous target variable to make predictions. Examples include Linear Regression, Decision Trees, Random Forests, and Support Vector Regression (SVR).
Classification: Classification algorithms are used to assign categorical labels or class memberships to new instances based on training data. The algorithm learns the patterns and relationships in the training data to classify new data points. Popular classification algorithms include Logistic Regression, Decision Trees, Random Forests, Naïve Bayes, and Support Vector Machines (SVM).
Ensemble Methods: Ensemble methods combine multiple base models to make more accurate predictions. They leverage the wisdom of crowds by aggregating the predictions of individual models. Examples of ensemble methods include Bagging (e.g., Random Forests), Boosting (e.g., AdaBoost, Gradient Boosting Machines), and Stacking.
Neural Networks: Neural networks are a class of models inspired by the structure and functioning of the human brain. They consist of interconnected nodes (neurons) organized into layers. Neural networks are powerful for modeling complex relationships and are used in applications such as image recognition, natural language processing, and time series analysis.
Instance-based Learning: Instance-based learning, also known as lazy learning, focuses on storing and retrieving training instances to make predictions. Algorithms like k-Nearest Neighbors (k-NN) classify new instances by finding the k nearest neighbors in the training data.
Supervised learning techniques are used for tasks such as predicting stock prices and sentiment analysis. To apply supervised learning effectively, it is important to have high-quality labeled training data, properly preprocess the data, select appropriate features, and tune the algorithm's hyperparameters. Additionally, regular model evaluation and validation are crucial to ensure the model's accuracy and generalization performance.
Supervised learning techniques are covered in more detail in module 4 of the CQF program.