From fd408b50297be21b6261d421b97a19314b58c4f5 Mon Sep 17 00:00:00 2001 From: Minyus Date: Thu, 15 Aug 2019 00:33:01 +0800 Subject: [PATCH] Update README.md --- README.md | 32 ++++++++++++++++---------------- 1 file changed, 16 insertions(+), 16 deletions(-) diff --git a/README.md b/README.md index 2c49af7..55683b4 100644 --- a/README.md +++ b/README.md @@ -127,22 +127,6 @@ Table data including the following columns:

-## Updates for version 1.x - -The latest version 1.x adopts [Kedro](https://kedro.readthedocs.io/) to add the following new -features and will be available soon in PyPI. - -- [Parallel execution] Train the 2 models in parallel -- [File management] Save and load intermediate files such as the trained models -- [Documentation] Generate the API document by Sphinx and visualize the process flow - -Other enhancements include: - -- [Logging] Show and/or log processing status such as timestamp and the running task -- [Model options] Specify models other than XGBoost and Logistic Regression for uplift -modeling and propensity modeling, respectively. - - ## How to install CausalLift? Option 1: install from the PyPI @@ -237,6 +221,22 @@ estimated_effect_df = cl.estimate_recommendation_impact() CausalLift flow diagram

+## New features introduced in version 1.0.0 + +CausalLift version 1.0.0 adopted [Kedro](https://kedro.readthedocs.io/) to add the following new +features. + +- [Parallel execution] Train the 2 models in parallel +- [File management] Save and load intermediate files such as the trained models +- [Documentation] Generate the API document by Sphinx and visualize the process flow + +Other enhancements include: + +- [Logging] Show and/or log processing status such as timestamp and the running task +- [Model options] Specify models other than XGBoost and Logistic Regression for uplift +modeling and propensity modeling, respectively. + + ## Details about the parameters Please see [[CausalLift API reference]](https://minyus.github.io/causallift/causallift.html).