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1. A machine learning technique that combines multiple models to improve the overall performance and accuracy of the prediction.
2. A supervised machine learning algorithm that classifies data into different classes by finding the hyperplane that best separates the data points.
3. A statistical method used to model binary outcomes by estimating the probability that a given outcome is present.
4. The first step in the predictive modelling process, involving gathering relevant data from various sources.
5. An ensemble learning method that constructs a multitude of decision trees at training time and outputs the mode of the classes as the prediction.
6. A technique used to assess the performance of a predictive model by splitting the data into multiple subsets.
7. The process of removing errors and inconsistencies from the collected data before further analysis.
8. A network of interconnected nodes, similar to neurons in the brain, that processes information by mimicking the way the human brain functions.
9. A flowchart-like structure in which each internal node represents a test on an attribute, each branch represents the outcome of the test, and each leaf node represents a class label.
10. A statistical method used to examine the relationship between one dependent variable and one or more independent variables.
11. The process of cleaning, transforming, and organizing data before building a predictive model.
12. A technique used to forecast future values based on past data points in time order.
13. The process of assessing the performance of the predictive model using validation data sets.
14. An ensemble learning method that builds a model in a stage-wise manner, with each new model addressing the errors of the previous models.
15. The stage where the predictive model is developed using the selected features and training data.
16. The process of choosing the most relevant input variables to be used in the predictive model.
17. A non-parametric method used for classification and regression that classifies a data point based on the majority class of its k nearest neighbors.