train_test_split( X, Y, test_size = 0.20, random_state = 5)
DFIND OUTLIERS IN HIGH DIMENSION INSTALL
model_selection import train_test_split #sklearn import does not automatically install sub packages X = boston_features2_df Y = boston_target_df X_train, X_test, Y_train, Y_test = sklearn. #Partition dataset import sklearn from sklearn. #Partition the data #Create training and test datasets #We know from prior work that two variables were not significant contributors and need to be removed (AGE and INDUS) # Drop AGE and INDUS boston_features2_df = boston_features_df. #Create dataframe from target boston_target_df = pd. #Create analytical data set #Create dataframe from feature_names boston_features_df = pd. datasets import load_boston boston = load_boston() #IMPORT DATASET import pandas as pd from sklearn. For this example, I removed two variables, AGE and INDUS, because they were not significant during the initial fitting procedure. Let’s look at the Boston Housing dataset and see if we can find outliers, leverage values and influential observations. Any value above the h cutoff may be considered a leverage value. Leverage statistic h can be used to spot high leverage values where the cutoff is (p+1)/n. In multiple regression models we can’t just simply look at x values within a variable and spot the leverage values because it is possible to have an observation within the normal range of each variable when the observation may be outside of the normal range when all regressors are considered simultaneously. As a result, just a few observations with high leverage may result in questionable model fit. As a result, removing a leverage value from the dataset will have an impact on the OLS line.
In other words, the observed value of a predictor is very unusual compared to other values. In contrast to an outlier, a leverage value has an unusual x observation. When studentized residuals values exceed 3 in absolute, we should be concerned about the observation being an outlier. Studentized residuals are computed by dividing each residual by the standard error. In order to aid the decision to deem an observation an outlier, studentized residuals may be used. Residual plots are a great way of visualizing outliers. Inclusion of outliers may also have an impact on R squared. As a consequence, outliers do have an impact on the interpretation of model fit. Recall that RSE is utilized for the computing of p-values and confidence intervals. Still, an outlier may cause significant issues as it does have an impact on RSE. In many cases, outliers do not have a large effect on the OLS line. This means that a predicted y value is far from the actual y value of an observation. Some observations are far from values predicted by a model and are called outliers. The location of observations in a space play a role in determining regression coefficients.
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