Index
A
- accuracy (defined), Binomial Classification
- activation functions, Activation Functions
- aggregation, Aggregating Rows
- algorithms
- ensembles, Ensembles-Categorical Ensembles
- improving results from, Helping the Models-Helping the Models
- as.h2o(), Load Directly from R
- AUC (Area Under Curve), Binomial Classification
- auto-encoder
- DL, Deep Learning Auto-Encoder-Stacked Auto-Encoder
- for training many-layered neural networks, Auto-Encoder
- stacked, Stacked Auto-Encoder-Stacked Auto-Encoder
B
- balance, of data, Split Data Already in H2O
- binomial classification
- about, Binomial Classification
- metrics for, Classification Metrics
- boosting, GBM and, Boosting-Boosting
- building energy efficiency test case
- comparison of algorithm results, Building Energy Results-Building Energy Results
- data columns, The Data Columns-The Data Columns
- data set setup and load, Setup and Load
- data sets, Data Set: Building Energy Efficiency-About the Data Set
- DL: default, Building Energy Efficiency: Default Deep Learning
- DL: results, Building Energy Results
- DL: tuned, Building Energy Efficiency: Tuned Deep Learning-Building Energy Efficiency: Tuned Deep Learning
- GBM: default, Building Energy Efficiency: Default GBM
- GBM: results, Building Energy Results
- GBM: tuned, Building Energy Efficiency: Tuned GBM-Building Energy Efficiency: Tuned GBM
- GLM: default, Building Energy Efficiency: Default GLM
- GLM: results, Building Energy Results
- GLM: tuned, Building Energy Efficiency: Tuned GLM-Building Energy Efficiency: Tuned GLM
- preliminary analysis of data
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