Index
A
- accumulators
- ACT
- Alternating Least Squares (ALS) algorithm / Collaborative filtering
- Apache Spark
- Apache Spark Notebooks
- attrition prediction
- attrition prediction, methods
- automation
- autoregressive-moving average (ARMA) / About time series
- autoregressive integrated moving average (ARIMA) / About time series
B
- Berkeley Data Analytics Stack (BDAS)
- broadcast variables
C
- churn prediction
- churn prediction, feature preparation
- churn prediction, methods
- cluster analysis
- confusion matrix
- Cross Industry Standard Process for Data Mining (CRISP-DM)
D
- data
- data and feature preparation
- Databricks notebook
- DataBricks Workspace
- data cleaning
- DataFrame
- dataframe API
- DataScientistWorkbench
- Data Scientist WorkBench
- dataset reorganization
- datasets
- datasets preprocessing
- data treatment, with SPSS
- decision tree
- decision trees
- deployment
- deployment, holistic view
- deployment, open data
- deployment, risk scoring
- Directed Acyclic Graph (DAG)
- distributed computing
E
F
- False Negative (Type I Error) / Model evaluation
- False Positive (FP) error rate / ROC
- False Positive (Type II Error) / Model evaluation
- false positive ratios
- feature
- feature development, Telco Data
- feature extraction
- feature preparation
- feature preparation, holistic view
- feature preparation, open data
- FORECAST R package
- fraud detection
G
H
I
- IBM Data Scientist Workbench
- IBM Predictive Extensions
- IBM SystemML
- identity matching
J
K
L
M
- machine learning
- machine learning (ML)
- machine learning algorithms
- machine learning methods, Telco Data
- methods, for holistic view
- methods, for recommendation
- methods, for risk scoring
- ML frameworks
- MLlib
- Mllib
- MLlib, parameters
- MLlib - PMML model export
- MLlib feature extraction
- MLlib guide
- ML workflows
- model deployment, Telco Data
- model estimation
- model estimation, holistic view
- model estimation, open data
- model estimation, recommendation
- model estimation, risk scoring
- model estimation, Telco Data
- model evaluation
- model evaluation, holistic view
- model evaluation, recommendation
- model evaluation, risk scoring
- model evaluation, Telco Data
N
O
P
R
- R
- Random forest
- Random Forest
- random forest
- Receiver Operating Characteristic curve (ROC) / ROC
- recommendation deployment
- recommendations, on Spark
- regression models
- repeatability
- ReporteRs R package
- Research Methods Four Elements (RM4Es)
- Resilient Distributed Dataset (RDD)
- results
- results, open data
- results, Telco Data
- results explanation
- results explanation, holistic view
- results explanation, risk scoring
- risk scoring
- R Markdown
- RMSE (Root-Mean-Square Error)
- RMSE calculation
- R notebook
- R notebooks
- R Notebooks implementation
- ROC (Receiver Operating Characteristic)
- ROCR
- Root Mean Square Error (RMSE)
- R package PMML
- R studio
S
- SampleClean
- service forecasting, Spark used
- shared variables
- Spark
- Spark, for recommendation engine
- Spark, for risk scoring
- spark-ts library
- Spark computing
- Spark computing framework
- Spark dataframe
- Spark DataSource API
- Spark implementation
- Spark MLlib
- Spark notebooks
- Spark pipeline
- Spark RDD
- SparkSQL
- Spark SQL
- SPSS Analytics Server
- SPSS Analytics server / SPSS on Spark – the SPSS Analytics server
- SPSS on Spark / SPSS on Spark
- SQLContext
- Structural Equation Modeling (SEM) / The SEM approach
- SystemML
T
- Telco Data
- time series modeling
- trends, visualizing
- True Positive (TP) error rate / ROC
Z
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