Braggart
发表于 2025-3-21 19:26:10
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colony
发表于 2025-3-22 00:03:03
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excursion
发表于 2025-3-22 02:37:39
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晚来的提名
发表于 2025-3-22 05:15:25
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遭遇
发表于 2025-3-22 12:42:14
Linear Algebra & Matrix Computing,is generally challenging to visualize complex data, e.g., large vectors, tensors, and tables in n-dimensional Euclidian spaces (. ≥ 3). Linear algebra allows us to mathematically represent, computationally model, statistically analyze, synthetically simulate, and visually summarize such complex data
Tdd526
发表于 2025-3-22 13:11:01
Dimensionality Reduction,ber of features when modeling a very large number of variables. Dimension reduction can help us extract a set of “uncorrelated” principal variables and reduce the complexity of the data. We are not simply picking some of the original variables. Rather, we are constructing new “uncorrelated” variable
Tdd526
发表于 2025-3-22 17:55:42
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prosperity
发表于 2025-3-23 00:39:17
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entail
发表于 2025-3-23 04:40:02
Decision Tree Divide and Conquer Classification,les. In some cases, we need to specify well stated rules for our decisions, just like a scoring criterion for driving ability or credit scoring for loan underwriting. The decisions in many situations actually require having a clear and easily understandable decision tree to follow the classification
Notorious
发表于 2025-3-23 08:58:00
Forecasting Numeric Data Using Regression Models, this Chapter, we will focus on specific model-based statistical methods providing forecasting and classification functionality. Specifically, we will (1) demonstrate the predictive power of multiple linear regression; (2) show the foundation of regression trees and model trees; and (3) examine two