Python evaluate_model示例

示例#1

def run(dataset, params, resultdir=".", use_params=True):
    pipeline_parameters = {}
    if use_params:
        pipeline_parameters[GaussianNB] = params

    pipeline_components = [RobustScaler, GaussianNB]
    return evaluate_model(dataset, pipeline_components, pipeline_parameters, resultdir=resultdir)

示例#2

def run(dataset, params, resultdir=".", use_params=True):
    pipeline_parameters = {}
    if use_params:
        pipeline_parameters[MultinomialNB] = params

    pipeline_components = [MinMaxScaler, MultinomialNB]
    return evaluate_model(dataset, pipeline_components, pipeline_parameters, resultdir=resultdir)

示例#3

文件： PassiveAggressiveClassifier.py 项目： sgpthomas/sklearn-pmlb-benchmarks

def run(dataset, params, resultdir=".", use_params=True):
    pipeline_parameters = {}
    if use_params:
        pipeline_parameters[PassiveAggressiveClassifier] = params

    pipeline_components = [RobustScaler, PassiveAggressiveClassifier]
    return evaluate_model(dataset,
                          pipeline_components,
                          pipeline_parameters,
                          resultdir=resultdir)

示例#4

文件： LogisticRegression.py 项目： sgpthomas/sklearn-pmlb-benchmarks

def run(dataset, params, resultdir=".", use_params=True):
    pipeline_parameters = {}
    if use_params:
        pipeline_parameters[LogisticRegression] = params

    pipeline_components = [RobustScaler, LogisticRegression]
    return evaluate_model(dataset,
                          pipeline_components,
                          pipeline_parameters,
                          resultdir=resultdir)