Data Mining Chapter 3: Supervised Learning Classifiers

Classification = A model or method for classifying new items. Regression = A set of k classes C = {c1, c2, …, ck} Training Set = A set of n labeled items D = {(d1, ci1), (d2, ci2), …, (dn cin)}. Supervised Learning = Predicting Oy values for new values on Ox axis using the regression function.

Validation Set = Calibration of some algorithms. Test Set = Classifying new items using the model/method. Training Set = Predicting Oy values for new values on Ox axis. Decision Tree = Visualizing the relationship between variables.

Linear Regression = Predicting Oy values for new values on Ox axis. Decision Tree = Play-tennis where weather conditions are used to decide if players may or may not start a new game. Classification = Predicting whether a person will buy a car or not. Supervised Learning = Image recognition using labeled images.

Training Set = A set of n labeled items D = {(d1, ci1), (d2, ci2), …, (dn cin)}. Validation Set = A set of k classes C = {c1, c2, …, ck}. Decision Tree = Nodes representing attributes, and edges representing the decision process. Regression Analysis = X and Y axes representing the independent and dependent variables.

Classification = To predict the class label of new items. Regression Analysis = To predict the continuous value of a dependent variable. Supervised Learning = To build a model that can make predictions on new data. Decision Tree = To visualize the relationship between variables.

Supervised Learning = Labeled data is used to train the model. Regression Analysis = The target variable is continuous. Classification = The target variable is categorical. Decision Tree = The model is non-parametric.

Classification = Image recognition, text classification, and bioinformatics. Regression Analysis = Predicting stock prices, energy consumption, and weather forecasting. Supervised Learning = Chatbots, sentiment analysis, and Recommender systems. Decision Tree = Customer segmentation, risk analysis, and medical diagnosis.

Classification = A table with a column for each attribute and the last column contains the class label. Regression Analysis = A matrix with the dependent variable in one column and independent variables in other columns. Supervised Learning = A dataset with labeled and unlabeled data. Decision Tree = A graph with nodes representing attributes and edges representing the decision process.

Classification = Accuracy, precision, recall, and F1 score. Regression Analysis = Mean Squared Error (MSE), Mean Absolute Error (MAE), and Coefficient of Determination (R-squared). Supervised Learning = Confusion matrix, ROC-AUC curve, and F1 score. Decision Tree = Accuracy, precision, and recall.

Classification = Predicting customer churn, credit risk assessment, and sentiment analysis. Regression Analysis = Predicting stock prices, energy consumption, and weather forecasting. Supervised Learning = Image recognition, natural language processing, and recommender systems. Decision Tree = Customer segmentation, risk analysis, and medical diagnosis.