Titlebook: Statistical Learning with Math and R; 100 Exercises for Bu Joe Suzuki Textbook 2020 The Editor(s) (if applicable) and The Author(s), under

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Unsupervised Learning,omponent analysis (PCA), a data analysis method that is often used for machine learning and multivariate analysis. For PCA, we consider another equivalent definition along with its mathematical meaning.

Grating

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火花

Linear Regression,ngle covariate (single regression) first and extend it to multiple covariates (multiple regression) later. Then, based on the statistical notion of estimating parameters from data, we find the distribution of the coefficients (estimates) obtained via the least squares method. Thus, we present a meth

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Resampling,statistical model to fit the data. However, we do not know whether the estimation result shows a satisfactory (prediction) performance for new data different from those used for the estimation. For example, in the forecasting of stock prices, even if the price movements up to yesterday are analyzed

陶醉

Information Criteria,inding rules from observational data is not limited to data science and statistics, However, many scientific discoveries are born through such processes. For example, the writing of the theory of elliptical orbits, the law of constant area velocity, and the rule of harmony in the theory of planetary

粗糙

AMOR

Nonlinear Regression,etween the covariates and response is not linear. In the case of linear regression in Chap. ., if there are . variables, we calculate . coefficients of the basis that consists of . functions .. This chapter addresses regression when the basis is general. For example, if the response is expressed as

RADE

Decision Trees,rom the root, each vertex traces to either the left or right at each branch, depending on whether a condition w.r.t. the covariates is met, and finally reaches a terminal node to obtain the response. Compared to the methods we have considered thus far, since it is expressed as a simple structure, th