homework 3 .docx - To introduce we try to define which model between linear regression principal component regression and Partial list square regression

# homework 3 .docx - To introduce we try to define which...

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To introduce we try to define which model between linear regression , principal component regression and Partial list square regression matches the best to estimate the relation between solubility and chemical structure. >data(solubility) load solubility data set >ls() data transformation stored Fingerprints are binary sequence of numbers which represents the presence or not of specific molecular substructure. Here we are looking of columns without binary. Then grep search for matches to column name that contain the patern “FP”. After that we find a correlation among 20 predictors and display the chart that show a correlation between number of atoms and the mol weight. Remove predictors that have very high correlations greater than the threshold value of 0.9 the random number seed is set prior to modeling so that the results can be reproduced We created a control function using 10 fold cross validation resampling technique

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Unformatted text preview: Create a linear regression model which fit with choose predictors lmTune tune is a function which hyperparameters of statistical methods using a grid search over supplied parameter ranges RMSE Rsquared show us that 87,93% of data is explained by the model. Build principal component model with tune function choose 35 rows on the grids RMSE is 0,73 and Rsquarred 0,8714 We ask to display PCR preventive model vs reality plsTune Run the function Partial Least Square To display RMSE and Rsquared of the ncomp choiced before. RMSE is 0,69 and Rsquared 0,88 with The plot show the correlation between predictive model and actual outcome. To conclude we can say that the PLS model which match the best with a prediction of 88.37% for only 20 components against PCR with Rsquared of 0,8714 with 35 components....
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