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feature-engineering

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nni
amommendes
amommendes commented Oct 12, 2021

Expected Behavior

Feature views should have the creation time (i.e., created_timestamp) at the first feast apply

Current Behavior

Features Views do not have creation time at feature view creation

Steps to reproduce

feast init fs
cd fs
feast apply
feast registry-dump
{
  "spec": {
    "name": "driver_id",
    "valueType": "INT64",
    "description": "driver 
evalml
angela97lin
angela97lin commented Oct 6, 2021

Right now, component_graph.get_component expects a string which is the unique name used to find a component in the graph (ex: "My Label Encoder", and not "Label Encoder"). This makes it difficult to find components of a specific class, ex LabelEncoder instances. We should add a method to help with this!

API should take into consideration what happens if we have multiple of the same component

Hyperactive

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

  • Updated Nov 29, 2020
  • Jupyter Notebook

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