##### Beginning of file
# This file was generated by PredictMD version 0.20.0
# For help, please visit https://www.predictmd.net
import PredictMD
### Begin project-specific settings
PredictMD.require_julia_version("v0.7.0")
PredictMD.require_predictmd_version("0.20.0")
# PredictMD.require_predictmd_version("0.20.0", "0.21.0-")
PROJECT_OUTPUT_DIRECTORY = PredictMD.project_directory(
homedir(),
"Desktop",
"breast_cancer_biopsy_example",
)
### End project-specific settings
### Begin logistic classifier code
display(logistic_calibration_curve)
PredictMD.probability_calibration_metrics(
logistic_classifier,
testing_features_df,
testing_labels_df,
single_label_name,
positive_class;
window = 0.1,
)
logistic_cutoffs, logistic_risk_group_prevalences =
PredictMD.risk_score_cutoff_values(
logistic_classifier,
testing_features_df,
testing_labels_df,
single_label_name,
positive_class;
average_function = mean,
)
@info(
string(
"Low risk: 0 to $(logistic_cutoffs[1]).",
" Medium risk: $(logistic_cutoffs[1]) to $(logistic_cutoffs[2]).",
" High risk: $(logistic_cutoffs[2]) to 1.",
)
)
@info(logistic_risk_group_prevalences)
logistic_cutoffs, logistic_risk_group_prevalences =
PredictMD.risk_score_cutoff_values(
logistic_classifier,
testing_features_df,
testing_labels_df,
single_label_name,
positive_class;
average_function = median,
)
@info(
string(
"Low risk: 0 to $(logistic_cutoffs[1]).",
" Medium risk: $(logistic_cutoffs[1]) to $(logistic_cutoffs[2]).",
" High risk: $(logistic_cutoffs[2]) to 1.",
)
)
@info(logistic_risk_group_prevalences)
logistic_classifier_filename = joinpath(
PROJECT_OUTPUT_DIRECTORY,
"logistic_classifier.jld2",
)
PredictMD.save_model(logistic_classifier_filename, logistic_classifier)
### End logistic classifier code
##### End of file
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