Release Note
Version 0.1.2
Experiment design - Support input random state to split dataset
Experiment list - Use reward metric in visualmap of hyperparams line chart(Fix bug)
** Other ** - Update hypergbm to 0.2.2
Version 0.1.1
Dataset manage
Search
Delete
- Upload or import CSV
Sampling analysis
Support no column headers
Inferring feature types
Dataset preview
Cat origin file on line
Scrolling
Dataset insight
Distribution of feature type
Data type, feature type, missing percentage, uniques, linear correlation
Recognize Id-ness, constant, missing percentage too high features
Feature search
- Datetime features
Display by year, month, day, hour, week
- Categorical features
Distribution of values
Mode
- Continuous features
Distribution of interval
Distribution of values
max, min, median, mean, stand deviation
Experiment design
Recommend experiment options
HyperGBM,HyperDT as experiment engine
Quick, performance training mode
Train-Validation-Holdout data partition
Split data in datetime order
Support binary classification, multi-classification, regression
Experiment list
Training process,
Remaining time estimation
Confusion matrix and ROC curve for binary-classification
- Evaluation metrics
Binary classification: Accuracy, F1, Fbeta, Precision, Recall, AUC, Log Loss
Multi-classification: Accuracy, F1, Fbeta, Precision, Recall, Log Loss
Regression: EVS, MAE, MSE, RMSE, MSLE, R2, MedianAE
View train log and source code
Export to notebook
Hyper-params
Batch predict