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DFFML Models For scikit / sklearn
About
Models created using scikit.
Install
$ python3 -m pip install --user dffml-model-scikit
Usage
- 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.
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