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DFFML Models For scikit / sklearn

About

Models created using scikit.

Install

$ python3 -m pip install --user dffml-model-scikit

Usage

  1. Linear Regression Model

For implementing linear regression to a dataset, let us take a simple example:

Years of Experience Expertise Trust Factor Salary
0 01 0.2 10
1 03 0.4 20
2 05 0.6 30
3 07 0.8 40
4 09 1.0 50
5 11 1.2 60
$ cat > train.csv << EOF
Years,Expertise,Trust,Salary
0,1,0.2,10
1,3,0.4,20
2,5,0.6,30
3,7,0.8,40
EOF
$ cat > test.csv << EOF
Years,Expertise,Trust,Salary
4,9,1.0,50
5,11,1.2,60
EOF
$ dffml train \
    -model scikitlr \
    -model-features Years:int:1 Expertise:int:1 Trust:float:1 \
    -model-predict Salary \
    -model-directory tempdir \
    -sources f=csv \
    -source-filename train.csv \
    -source-readonly \
    -log debug
$ dffml accuracy \
    -model scikitlr \
    -model-features Years:int:1 Expertise:int:1 Trust:float:1 \
    -model-predict Salary \
    -model-directory tempdir \
    -sources f=csv \
    -source-filename test.csv \
    -source-readonly \
    -log debug
$ echo -e 'Years,Expertise,Trust\n6,13,1.4\n' | \
  dffml predict all \
    -model scikitlr \
    -model-features Years:int:1 Expertise:int:1 Trust:float:1 \
    -model-predict Salary \
    -model-directory tempdir \
    -sources f=csv \
    -source-filename /dev/stdin \
    -source-readonly \
    -log debug

License

Scikit Models are distributed under the terms of the MIT License.

Project details


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