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sakshamarora1John Andersen
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docs: model: Replace -features with -model-features
 Signed-off-by: John Andersen <[email protected]>
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‎dffml/skel/model/README.md‎

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@@ -20,7 +20,7 @@ dffml train \
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-model model_name \
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-sources csv=iris_training.csv \
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-classifications 0 1 2 \
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-features \
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-model-features \
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def:SepalLength:float:1 \
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def:SepalWidth:float:1 \
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def:PetalLength:float:1 \
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-model model_name \
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-sources csv=iris_training.csv \
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-classifications 0 1 2 \
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-features \
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-model-features \
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def:SepalLength:float:1 \
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def:SepalWidth:float:1 \
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def:PetalLength:float:1 \
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-model model_name \
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-sources csv=iris_test.csv \
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-classifications 0 1 2 \
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-features \
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-model-features \
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def:SepalLength:float:1 \
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def:SepalWidth:float:1 \
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def:PetalLength:float:1 \

‎docs/plugins/dffml_model.rst‎

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@@ -386,7 +386,7 @@ Training:
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$ dffml train \
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-model SCIKIT_MODEL_ENTRYPOINT \
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-features FEATURE_DEFINITION \
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-model-features FEATURE_DEFINITION \
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-model-predict TO_PREDICT \
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-model-SCIKIT_PARAMETER_NAME SCIKIT_PARAMETER_VALUE \
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-sources f=TRAINING_DATA_SOURCE_TYPE \
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$ dffml accuracy \
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-model SCIKIT_MODEL_ENTRYPOINT \
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-features FEATURE_DEFINITION \
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-model-features FEATURE_DEFINITION \
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-model-predict TO_PREDICT \
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-sources f=TESTING_DATA_SOURCE_TYPE \
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-source-filename TESTING_DATA_FILE_NAME \
@@ -413,7 +413,7 @@ Predicting with trained model:
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$ dffml predict all \
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-model SCIKIT_MODEL_ENTRYPOINT \
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-features FEATURE_DEFINITION \
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-model-features FEATURE_DEFINITION \
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-model-predict TO_PREDICT \
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-sources f=PREDICT_DATA_SOURCE_TYPE \
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-source-filename PREDICT_DATA_FILE_NAME \
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EOF
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$ dffml train \
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-model scikitlr \
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-features def:Years:int:1 def:Expertise:int:1 def:Trust:float:1 \
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-model-features def:Years:int:1 def:Expertise:int:1 def:Trust:float:1 \
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-model-predict Salary \
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-sources f=csv \
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-source-filename train.csv \
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-source-readonly \
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-log debug
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$ dffml accuracy \
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-model scikitlr \
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-features def:Years:int:1 def:Expertise:int:1 def:Trust:float:1 \
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-model-features def:Years:int:1 def:Expertise:int:1 def:Trust:float:1 \
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-model-predict Salary \
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-sources f=csv \
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-source-filename test.csv \
@@ -502,7 +502,7 @@ Let us take a simple example:
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$ echo -e 'Years,Expertise,Trust\n6,13,1.4\n' | \
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dffml predict all \
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-model scikitlr \
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-features def:Years:int:1 def:Expertise:int:1 def:Trust:float:1 \
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-model-features def:Years:int:1 def:Expertise:int:1 def:Trust:float:1 \
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-model-predict Salary \
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-sources f=csv \
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-source-filename /dev/stdin \

‎docs/usage/integration.rst‎

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@@ -376,7 +376,7 @@ to train on our dataset.
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-model-classification maintained \
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-model-classifications 0 1 \
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-sources db=demoapp \
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-features \
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-model-features \
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def:authors:int:10 \
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def:commits:int:10 \
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def:work:int:10 \
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-model-classification maintained \
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-model-classifications 0 1 \
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-sources db=demoapp \
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-features \
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-model-features \
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def:authors:int:10 \
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def:commits:int:10 \
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def:work:int:10 \
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-model-classification maintained \
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-model-classifications 0 1 \
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-sources db=demoapp \
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-features \
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-model-features \
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def:authors:int:10 \
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def:work:int:10 \

‎examples/maintained/cgi-bin/api-ml.py‎

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@@ -53,7 +53,7 @@
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'-model-classification', 'maintained',
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'-model-classifications', '0', '1',
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'-sources', 'db=demoapp',
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'-features',
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'-model-features',
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'def:authors:int:10',
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'def:commits:int:10',
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'def:work:int:10',

‎model/scikit/dffml_model_scikit/__init__.py‎

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$ dffml train \\
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-model SCIKIT_MODEL_ENTRYPOINT \\
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-features FEATURE_DEFINITION \\
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-model-features FEATURE_DEFINITION \\
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-model-predict TO_PREDICT \\
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-model-SCIKIT_PARAMETER_NAME SCIKIT_PARAMETER_VALUE \\
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-sources f=TRAINING_DATA_SOURCE_TYPE \\
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$ dffml accuracy \\
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-model SCIKIT_MODEL_ENTRYPOINT \\
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-features FEATURE_DEFINITION \\
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-model-features FEATURE_DEFINITION \\
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-model-predict TO_PREDICT \\
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-sources f=TESTING_DATA_SOURCE_TYPE \\
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-source-filename TESTING_DATA_FILE_NAME \\
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$ dffml predict all \\
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-model SCIKIT_MODEL_ENTRYPOINT \\
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-features FEATURE_DEFINITION \\
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-model-features FEATURE_DEFINITION \\
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-model-predict TO_PREDICT \\
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-sources f=PREDICT_DATA_SOURCE_TYPE \\
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-source-filename PREDICT_DATA_FILE_NAME \\
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EOF
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$ dffml train \\
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-model scikitlr \\
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-features def:Years:int:1 def:Expertise:int:1 def:Trust:float:1 \\
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-model-features def:Years:int:1 def:Expertise:int:1 def:Trust:float:1 \\
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-model-predict Salary \\
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-sources f=csv \\
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-source-filename train.csv \\
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-source-readonly \\
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-log debug
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$ dffml accuracy \\
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-model scikitlr \\
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-features def:Years:int:1 def:Expertise:int:1 def:Trust:float:1 \\
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-model-features def:Years:int:1 def:Expertise:int:1 def:Trust:float:1 \\
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-model-predict Salary \\
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-sources f=csv \\
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-source-filename test.csv \\
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$ echo -e 'Years,Expertise,Trust\\n6,13,1.4\\n' | \\
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dffml predict all \\
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-model scikitlr \\
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-features def:Years:int:1 def:Expertise:int:1 def:Trust:float:1 \\
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-model-features def:Years:int:1 def:Expertise:int:1 def:Trust:float:1 \\
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-model-predict Salary \\
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-sources f=csv \\
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-source-filename /dev/stdin \\

‎model/scratch/README.md‎

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EOF
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$ dffml train -model scratchslr -features def:Years:int:1 -model-predict Salary -sources f=csv -source-filename dataset.csv -source-readonly -log debug
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$ dffml accuracy -model scratchslr -features def:Years:int:1 -model-predict Salary -sources f=csv -source-filename dataset.csv -source-readonly -log debug
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$ dffml train -model scratchslr -model-features def:Years:int:1 -model-predict Salary -sources f=csv -source-filename dataset.csv -source-readonly -log debug
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$ dffml accuracy -model scratchslr -model-features def:Years:int:1 -model-predict Salary -sources f=csv -source-filename dataset.csv -source-readonly -log debug
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1.0
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$ echo -e 'Years,Salary\n6,0\n' | dffml predict all -model scratchslr -features def:Years:int:1 -model-predict Salary -sources f=csv -source-filename /dev/stdin -source-readonly -log debug
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$ echo -e 'Years,Salary\n6,0\n' | dffml predict all -model scratchslr -model-features def:Years:int:1 -model-predict Salary -sources f=csv -source-filename /dev/stdin -source-readonly -log debug
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[
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{
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"extra": {},

‎news/origin_master_August_15_2019.txt‎

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EOF
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$ dffml train \
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-model scratchslr \
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-features def:Years:int:1 \
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-model-features def:Years:int:1 \
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-model-predict Salary \
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-sources f=csv \
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-log debug
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$ dffml accuracy \
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-model scratchslr \
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-features def:Years:int:1 \
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-model-features def:Years:int:1 \
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-model-predict Salary \
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-sources f=csv \
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$ echo -e 'Years,Salary\n6,0\n' | \
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dffml predict all \
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-model scratchslr \
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-features def:Years:int:1 \
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-model-features def:Years:int:1 \
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-model-predict Salary \
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-sources f=csv \
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-source-filename /dev/stdin \

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