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Create & train a logistic regression model using k-fold cross-validation/ leave-one-out cross-validation
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README.md

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- PyCharm 2022.2 (Professional Edition)
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- Python 3.7 or above
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### Datasets Resource:
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1. **[Kaggle](https://www.kaggle.com/uciml/pima-indians-diabetes-database)**
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2. **[UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/index.php)**
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___
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### References
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{
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"Outputs" : {
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"target" : {
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"MLFeatureShortDescription" : "--"
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},
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"classProbability" : {
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"MLFeatureShortDescription" : "--"
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}
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},
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"Inputs" : {
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"sepal length (cm)" : {
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"MLFeatureShortDescription" : "--"
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},
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"sepal width (cm)" : {
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"MLFeatureShortDescription" : "--"
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},
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"petal length (cm)" : {
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"MLFeatureShortDescription" : "--"
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},
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"petal width (cm)" : {
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"MLFeatureShortDescription" : "--"
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}
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},
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"TrainingInputs" : {
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}
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}
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{
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"MLModelVersionStringKey" : "--",
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"MLModelDescriptionKey" : "--",
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"MLModelAuthorKey" : "--",
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"MLModelLicenseKey" : "--"
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}

usecases/tabular_classifier/decision_tree_classifer/iris_flowers_dtm.mlpackage/Manifest.json

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"description": "CoreML Model Specification",
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"name": "model.mlmodel",
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"path": "com.apple.CoreML/model.mlmodel"
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},
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"9F8A6355-20F8-474B-955A-1B8072AE287B": {
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"author": "com.apple.CoreML",
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"description": "External FeatureDescription Overlay",
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"name": "FeatureDescriptions.json",
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"path": "com.apple.CoreML/FeatureDescriptions.json"
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},
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"C1A62F80-57C4-43DE-BBD3-16E6BE8CB283": {
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"author": "com.apple.CoreML",
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"description": "External Metadata Overlay",
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"name": "Metadata.json",
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"path": "com.apple.CoreML/Metadata.json"
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}
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},
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"rootModelIdentifier": "7360340A-F6CF-46AC-99E4-3E2868896A7B"

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