Validation curve
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I'm learning about data science and I've been checking several tutorials. Now I'm trying some validation curves on the problem sample I'm resolving and I'm having some troubles with it. This is the code:
from sklearn.model_selection import validation_curve
degree = np.arange(0, 5)
train_score, val_score = validation_curve(poly_model, BBrentt, Petrol7['FValues'],'polynomialfeatures__degree', degree, cv=5)
plt.plot(degree, np.median(train_score, 1), color='blue', label='training score')
plt.plot(degree, np.median(val_score, 1), color='red', label='validation score')
plt.legend(loc='best')
plt.ylim(0, 1)
plt.xlabel('degree')
plt.ylabel('score');
print(train_score)
print(val_score) '
poly_model is the polinomial regression I did earlier, BBrentt and Petrol7['FValues'] are the data i'm using, they're fine i believe. Here's a picture of the results I get and a pic of the regression and the data I used earlier. What am I doing wrong? Because clearly the validation score is too low
python cross-validation training
asked 18 hours ago
Armando DelgadoArmando Delgado
1
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Armando Delgado is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
I'm learning about data science and I've been checking several tutorials. Now I'm trying some validation curves on the problem sample I'm resolving and I'm having some troubles with it. This is the code:
from sklearn.model_selection import validation_curve
degree = np.arange(0, 5)
train_score, val_score = validation_curve(poly_model, BBrentt, Petrol7['FValues'],'polynomialfeatures__degree', degree, cv=5)
plt.plot(degree, np.median(train_score, 1), color='blue', label='training score')
plt.plot(degree, np.median(val_score, 1), color='red', label='validation score')
plt.legend(loc='best')
plt.ylim(0, 1)
plt.xlabel('degree')
plt.ylabel('score');
print(train_score)
print(val_score) '
poly_model is the polinomial regression I did earlier, BBrentt and Petrol7['FValues'] are the data i'm using, they're fine i believe. Here's a picture of the results I get and a pic of the regression and the data I used earlier. What am I doing wrong? Because clearly the validation score is too low
python cross-validation training
asked 18 hours ago
Armando DelgadoArmando Delgado
1
New contributor
Armando Delgado is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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It doesn't look like you've done any preprocessing on the data. Perhaps consider scaling the data within your cross validation. You can put the scaler in aPipelinewith your estimator and then use the pipeline within the cross-validation function. I'm not sure if this will help, but you can at least eliminate it as a problem.
$endgroup$
– Wes
16 hours ago
add a comment |
$begingroup$
I'm learning about data science and I've been checking several tutorials. Now I'm trying some validation curves on the problem sample I'm resolving and I'm having some troubles with it. This is the code:
from sklearn.model_selection import validation_curve
degree = np.arange(0, 5)
train_score, val_score = validation_curve(poly_model, BBrentt, Petrol7['FValues'],'polynomialfeatures__degree', degree, cv=5)
plt.plot(degree, np.median(train_score, 1), color='blue', label='training score')
plt.plot(degree, np.median(val_score, 1), color='red', label='validation score')
plt.legend(loc='best')
plt.ylim(0, 1)
plt.xlabel('degree')
plt.ylabel('score');
print(train_score)
print(val_score) '
poly_model is the polinomial regression I did earlier, BBrentt and Petrol7['FValues'] are the data i'm using, they're fine i believe. Here's a picture of the results I get and a pic of the regression and the data I used earlier. What am I doing wrong? Because clearly the validation score is too low
python cross-validation training
asked 18 hours ago
Armando DelgadoArmando Delgado
1
New contributor
Armando Delgado is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I'm learning about data science and I've been checking several tutorials. Now I'm trying some validation curves on the problem sample I'm resolving and I'm having some troubles with it. This is the code:
from sklearn.model_selection import validation_curve
degree = np.arange(0, 5)
train_score, val_score = validation_curve(poly_model, BBrentt, Petrol7['FValues'],'polynomialfeatures__degree', degree, cv=5)
plt.plot(degree, np.median(train_score, 1), color='blue', label='training score')
plt.plot(degree, np.median(val_score, 1), color='red', label='validation score')
plt.legend(loc='best')
plt.ylim(0, 1)
plt.xlabel('degree')
plt.ylabel('score');
print(train_score)
print(val_score) '
poly_model is the polinomial regression I did earlier, BBrentt and Petrol7['FValues'] are the data i'm using, they're fine i believe. Here's a picture of the results I get and a pic of the regression and the data I used earlier. What am I doing wrong? Because clearly the validation score is too low
python cross-validation training
python cross-validation training
asked 18 hours ago
Armando DelgadoArmando Delgado
1
New contributor
Armando Delgado is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 18 hours ago
Armando DelgadoArmando Delgado
1
New contributor
Armando Delgado is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 18 hours ago
Armando DelgadoArmando Delgado
1
New contributor
Armando Delgado is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 18 hours ago
Armando DelgadoArmando Delgado
1
asked 18 hours ago
Armando DelgadoArmando Delgado
1
1
New contributor
Armando Delgado is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Armando Delgado is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Armando Delgado is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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It doesn't look like you've done any preprocessing on the data. Perhaps consider scaling the data within your cross validation. You can put the scaler in aPipelinewith your estimator and then use the pipeline within the cross-validation function. I'm not sure if this will help, but you can at least eliminate it as a problem.
$endgroup$
– Wes
16 hours ago
add a comment |
$begingroup$
It doesn't look like you've done any preprocessing on the data. Perhaps consider scaling the data within your cross validation. You can put the scaler in aPipelinewith your estimator and then use the pipeline within the cross-validation function. I'm not sure if this will help, but you can at least eliminate it as a problem.
$endgroup$
– Wes
16 hours ago
$begingroup$
It doesn't look like you've done any preprocessing on the data. Perhaps consider scaling the data within your cross validation. You can put the scaler in a
Pipeline with your estimator and then use the pipeline within the cross-validation function. I'm not sure if this will help, but you can at least eliminate it as a problem.$endgroup$
– Wes
16 hours ago
$begingroup$
It doesn't look like you've done any preprocessing on the data. Perhaps consider scaling the data within your cross validation. You can put the scaler in a
Pipeline with your estimator and then use the pipeline within the cross-validation function. I'm not sure if this will help, but you can at least eliminate it as a problem.$endgroup$
– Wes
16 hours ago
add a comment |
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It doesn't look like you've done any preprocessing on the data. Perhaps consider scaling the data within your cross validation. You can put the scaler in a
Pipelinewith your estimator and then use the pipeline within the cross-validation function. I'm not sure if this will help, but you can at least eliminate it as a problem.$endgroup$
– Wes
16 hours ago