ML: Model tuning suggestions please -












-2












$begingroup$


Why am I getting so low scores, even though I've removed the blank and NaN values from my data set? 



models = 

models.append(('LDA', LinearDiscriminantAnalysis()))

models.append(('KNN', KNeighborsClassifier()))

models.append(('CART', DecisionTreeClassifier()))

models.append(('NB', GaussianNB()))

models.append(('SVM', SVC(gamma='auto')))



for name, model in models:

    kfold = model_selection.KFold(n_splits=10, random_state=seed)

    cv_results = model_selection.cross_val_score(model, x_train, y_train, cv=kfold, scoring=scoring)

    results.append(cv_results)

    names.append(name)

    msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())

    print(msg)


Output -



LDA: 0.341045 (0.012459)

KNN: 0.539492 (0.023726)

CART: 0.587322 (0.012348)

NB: 0.335151 (0.019190)

SVM: 0.589792 (0.011770)


Also, my accuracy score is quite low, using the Decision Tree Classifier with default hyperparameters cart = DecisionTreeClassifier(random_state=100) -



Accuracy Score (using CART)=> 0.586663673102829


Ideas please?






machine-learning classification scikit-learn pandas hyperparameter-tuning







share|improve this question









$endgroup$












  • $begingroup$
    Focus on features first and modelling secondly
    $endgroup$
    – Aditya
    4 hours ago
















-2












$begingroup$


Why am I getting so low scores, even though I've removed the blank and NaN values from my data set? 



models = 

models.append(('LDA', LinearDiscriminantAnalysis()))

models.append(('KNN', KNeighborsClassifier()))

models.append(('CART', DecisionTreeClassifier()))

models.append(('NB', GaussianNB()))

models.append(('SVM', SVC(gamma='auto')))



for name, model in models:

    kfold = model_selection.KFold(n_splits=10, random_state=seed)

    cv_results = model_selection.cross_val_score(model, x_train, y_train, cv=kfold, scoring=scoring)

    results.append(cv_results)

    names.append(name)

    msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())

    print(msg)


Output -



LDA: 0.341045 (0.012459)

KNN: 0.539492 (0.023726)

CART: 0.587322 (0.012348)

NB: 0.335151 (0.019190)

SVM: 0.589792 (0.011770)


Also, my accuracy score is quite low, using the Decision Tree Classifier with default hyperparameters cart = DecisionTreeClassifier(random_state=100) -



Accuracy Score (using CART)=> 0.586663673102829


Ideas please?






machine-learning classification scikit-learn pandas hyperparameter-tuning







share|improve this question









$endgroup$












  • $begingroup$
    Focus on features first and modelling secondly
    $endgroup$
    – Aditya
    4 hours ago














-2












-2








-2





$begingroup$


Why am I getting so low scores, even though I've removed the blank and NaN values from my data set? 



models = 

models.append(('LDA', LinearDiscriminantAnalysis()))

models.append(('KNN', KNeighborsClassifier()))

models.append(('CART', DecisionTreeClassifier()))

models.append(('NB', GaussianNB()))

models.append(('SVM', SVC(gamma='auto')))



for name, model in models:

    kfold = model_selection.KFold(n_splits=10, random_state=seed)

    cv_results = model_selection.cross_val_score(model, x_train, y_train, cv=kfold, scoring=scoring)

    results.append(cv_results)

    names.append(name)

    msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())

    print(msg)


Output -



LDA: 0.341045 (0.012459)

KNN: 0.539492 (0.023726)

CART: 0.587322 (0.012348)

NB: 0.335151 (0.019190)

SVM: 0.589792 (0.011770)


Also, my accuracy score is quite low, using the Decision Tree Classifier with default hyperparameters cart = DecisionTreeClassifier(random_state=100) -



Accuracy Score (using CART)=> 0.586663673102829


Ideas please?






machine-learning classification scikit-learn pandas hyperparameter-tuning







share|improve this question









$endgroup$




Why am I getting so low scores, even though I've removed the blank and NaN values from my data set? 



models = 

models.append(('LDA', LinearDiscriminantAnalysis()))

models.append(('KNN', KNeighborsClassifier()))

models.append(('CART', DecisionTreeClassifier()))

models.append(('NB', GaussianNB()))

models.append(('SVM', SVC(gamma='auto')))



for name, model in models:

    kfold = model_selection.KFold(n_splits=10, random_state=seed)

    cv_results = model_selection.cross_val_score(model, x_train, y_train, cv=kfold, scoring=scoring)

    results.append(cv_results)

    names.append(name)

    msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())

    print(msg)


Output -



LDA: 0.341045 (0.012459)

KNN: 0.539492 (0.023726)

CART: 0.587322 (0.012348)

NB: 0.335151 (0.019190)

SVM: 0.589792 (0.011770)


Also, my accuracy score is quite low, using the Decision Tree Classifier with default hyperparameters cart = DecisionTreeClassifier(random_state=100) -



Accuracy Score (using CART)=> 0.586663673102829


Ideas please?






machine-learning classification scikit-learn pandas hyperparameter-tuning




machine-learning classification scikit-learn pandas hyperparameter-tuning






share|improve this question













share|improve this question











share|improve this question




share|improve this question










asked 5 hours ago









ranit.branit.b

165




165












  • $begingroup$
    Focus on features first and modelling secondly
    $endgroup$
    – Aditya
    4 hours ago


















  • $begingroup$
    Focus on features first and modelling secondly
    $endgroup$
    – Aditya
    4 hours ago
















$begingroup$
Focus on features first and modelling secondly
$endgroup$
– Aditya
4 hours ago




$begingroup$
Focus on features first and modelling secondly
$endgroup$
– Aditya
4 hours ago










0






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