mu = 12.02 and sigma = 0.40 Total Percent PoolQC 2908 0.996915 MiscFeature 2812 0.964004 Alley 2719 0.932122 Fence 2346 0.804251 FireplaceQu 1420 0.486802 LotFrontage 486 0.166610 GarageCond 159 0.054508 GarageQual 159 0.054508 GarageYrBlt 159 0.054508 GarageFinish 159 0.054508 GarageType 157 0.053822 BsmtCond 82 0.028111 BsmtExposure 82 0.028111 BsmtQual 81 0.027768 BsmtFinType2 80 0.027425 BsmtFinType1 79 0.027083 MasVnrType 24 0.008228 MasVnrArea 23 0.007885 MSZoning 4 0.001371 BsmtHalfBath 2 0.000686 Functional 2 0.000686 BsmtFullBath 2 0.000686 BsmtFinSF2 1 0.000343 BsmtUnfSF 1 0.000343 BsmtFinSF1 1 0.000343 Exterior2nd 1 0.000343 TotalBsmtSF 1 0.000343 Exterior1st 1 0.000343 SaleType 1 0.000343 Electrical 1 0.000343 KitchenQual 1 0.000343 GarageCars 1 0.000343 GarageArea 1 0.000343 ElasticNetCV(alphas=[0.0001, 0.0005, 0.001, 0.01, 0.1, 1, 10], copy_X=True, cv=None, eps=0.001, fit_intercept=True, l1_ratio=[0.01, 0.1, 0.5, 0.9, 0.99], max_iter=5000, n_alphas=100, n_jobs=1, normalize=False, positive=False, precompute='auto', random_state=None, selection='cyclic', tol=0.0001, verbose=0) R2: 0.935715193163475 RMSE: 0.09642491303900429 RMSLE: 0.007523339667622065 Test R2: 0.9220212454308224 RMSE: 0.11007238496371424 RMSLE: 0.008734648379211523 Accuracy: 0.92 (+/- 0.03) GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None, learning_rate=0.05, loss='huber', max_depth=3, max_features='sqrt', max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=15, min_samples_split=10, min_weight_fraction_leaf=0.0, n_estimators=3000, presort='auto', random_state=None, subsample=1.0, verbose=0, warm_start=False) R2: 0.9711309745182541 RMSE: 0.06507494627422042 RMSLE: 0.00516170160440001 Test R2: 0.876227635919166 RMSE: 0.1311545087701359 RMSLE: 0.010440612058522925 Accuracy: 0.91 (+/- 0.03) XGBRegressor(base_score=0.5, colsample_bylevel=1, colsample_bytree=1, gamma=0, learning_rate=0.05, max_delta_step=0, max_depth=3, min_child_weight=1, missing=None, n_estimators=3000, nthread=-1, objective='reg:linear', reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=0, silent=True, subsample=1) R2: 0.998102033663671 RMSE: 0.01716734991141679 RMSLE: 0.0013246240985368375 Test R2: 0.8912268252304474 RMSE: 0.12701108893000432 RMSLE: 0.010024565631835718 Accuracy: 0.91 (+/- 0.04)