## Recently Published

##### Stacking Mean LR & XGB - Pred & Perc Error

Stacking Mean Linear Regressione & XGBoost - Pred & Perc Error

##### Stacking Mean LR & XGB - Perc Error

Stacking with mean of Linear Regressione and XGBOOST

##### Comparison of test data sets and subset (Normalized with std dev) giving the worst cases for all model

Comparison of test data sets and subset giving the worst cases for all model:
- datasets (SNS with 0) normalized by dividing variables by the std dev of m1, .., m11
- Worst Cases: Error > 50% for all models

##### Model Comparison - Normalized dataset (v2)

Normalization of datasets by dividing variables by the standard deviation of m1, … , m11

##### Comparison of test data sets and subset (Normalized) giving the worst cases for all model

Comparison of test data sets and subset giving the worst cases for all model
- datasets (SNS with 0) normalized by dividing variables by the max of m1, .., m11
- Worst Cases: Error > 50% for all models

##### Model Comparison - Normalized dataset

Normalization of datasets by dividing variables by the max of m1, .., m11

##### Comparison of data sets giving the worst cases for each of four model (SNS with 0)

Comparison of data sets giving the worst cases (Perc Error > 50) for each of four model (SNS with 0)

##### Model Stacking - Mean of the best 3 Models (SNS with 0)

Mean of the best 3 models on dataset with 0
- H2O XGBOOST
- Linear Regression
- Random Forest

##### Model Stacking - Mean of the best 3 Models (SNS without 0)

Mean of the best 3 models on dataset without 0
- Linear Regression
- Random Forest Regression
- Gradient Boosted Trees Regression

##### Naïve Models - SNS Without 0

Model 1: Pred = mean(m1 ... m11)
Model 2: Pred = m11

##### Naïve Models - SNS With 0

Model 1: Pred = mean(m1 ... m11)
Model 2: Pred = m11