IBF Next Level Forecasting by Ceres Analytics
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Data
EDA
Linear Reg
Cluster
Logistic Reg
Decision Tree
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Provided by Ceres Analytics, LLC, without warranty, express or implied
Intended for learning purposes only
Works best in Google Chrome browser
WELCOME
Tools for IBF's Next Level Forecasting Analytics
Data Source
Built-in
Upload CSV (comma-delimited)
Upload TXT,TSV (tab-delimited)
Column Headers names must be in row 1 (no quotes)
Any Dependent Variable must be in Column 1
5 MB limit
Choose Built-in Dataset
Quarterly Sales
Telecom
Coffee
Challenger Launch
Cars
Upload a file
Browse...
Upload a file
Browse...
Please specify one:
Sequential Time Variable
Choose unique row identifier
Choose how to explore
Time series plot
Histogram (1-D)
2-D scatter plot
Contingency table
Choose variables (up to 12)
Choose Y (vertical axis or dependent variable)
Choose X (horizontal axis or independent variable)
Run!
Choose regression type
OLS
Stepwise (bidirectional)
All Possible Subsets
Lasso
Choose Y (scalar dependent variable)
Choose Xs (horizontal axis or independent variables)
SLE (Signicance Level to Enter)
SLS (Signicance Level to Stay)
Choose Xs (independent variables, up to 12)
Max # models to show (of each 1-X, 2-X, etc.)
Criterion for model selection
adjRsq
AIC
BIC
Criterion for model selection
lambda.min
lambda.1se
Model version post-selection for testing (ex post)
Lasso
OLS
How many holdouts (ex post)
Run!
Choose variables (up to 12)
Run!
Evaluate stats for this range of clusters:
Minimum #
Maximum #
Evaluate!
Assign Cluster #s (based on how many)
Cut!
Choose regression type
Logistic
Stepwise logistic (bidirectional)
Choose Y (binary dependent variable)
Choose X (numeric explanatory variables)
SLE (Signicance Level to Enter)
SLS (Signicance Level to Stay)
Run!
Cutoff Probability (>= is Positive)
Apply this cutoff!
Choose tree type
Classification tree
Regression tree
Choose Y (class dependent variable)
Choose Y (scalar dependent variable)
Choose X (explanatory variables)
How many holdouts
Minimum split size
Minimum bucket size
Maximum tree depth
Initial CP
Run!
Cutoff Probability (>= is Positive)
Apply this cutoff!
Training:
Test: