Hi Andy,
Yes here is the full code in which I am having a training dataset (x_data)
and an independent test dataset(test_x_data).
Mose importantly, I found few such value in iris data too.
#same Scaling on both test and train data (centering the data scaling)
scaler = preprocessing.StandardScaler()
x_data = scaler.fit_transform(x_data)
test_x_data = scaler.transform(test_x_data)
np.random.seed(0)
indices = np.random.permutation(len(x_data))
X_train = x_data[indices]
y_train = y_data[indices]
np.random.seed(0)
indices = np.random.permutation(len(test_x_data))
X_test = test_x_data[indices]
y_test = test_y_data[indices]
#For Random Forest
clf = RandomForestClassifier(n_estimators=40)
scores = clf.fit(X_train, y_train).score(X_test, y_test)
y_pred = clf.predict(X_test)
print "y_pred", y_pred
y_score = clf.fit(X_train, y_train).predict_proba(X_test)
y_score = np.around(y_score, decimals=2)
accurate = accuracy_score(y_test, y_pred)
prec = precision_score(y_test, y_pred, average='micro')
rec = recall_score(y_test, y_pred, average='micro')
fscore = fbeta_score(y_test, y_pred, average='micro', beta=0.5)
areaRoc = roc_auc_score(y_test, y_score[:,1])
#Generate ROC curve for each cross-validation
fpr, tpr, thresholds = roc_curve(y_test, y_score[:,1], pos_label = 1)
#Pos level for positive class
precision, recall, threshold = precision_recall_curve(y_test,
y_score[:,1])
random_mean_auc_10 = auc(fpr, tpr)
plt.plot([0, 1], [0, 1], '--', color=(0.6, 0.6, 0.6), label='Standard')
plt.plot(fpr, tpr, 'k--',label='RF_ROC_all_data (area = %0.2f)' %
random_mean_auc_10, lw=2, color=(0.45, 0.42, 0.18)) #Plot mean ROC area in
cross validation
plt.xlim([-0.05, 1.05])
plt.ylim([-0.05, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic example')
plt.legend(loc="lower right")
pl.savefig(out_folder + "/117_misclassified_RF_FANTOM_
validation_featureSelected_top5_tssDist_H3K27me3_H3K36me3.png",
transparent=True, bbox_inches='tight', pad_inches=0.2)
plt.show()
Please let me know if you need further information.
Thanks!
Shalu
On Wed, Feb 25, 2015 at 9:21 PM, Andy <t3k...@gmail.com> wrote:
> Hi Shalu.
> Can you give your code. The prediction is just the argmax of
> predict_proba, so I'd be very surprised if they are not consistent.
>
> Cheers,
> Andy
>
>
> On 02/25/2015 08:33 AM, shalu jhanwar wrote:
>
> Hi all,
>
> I'm facing the same problem with predict_proba for Random_forest
> classifier. I want to get a confidence value for each class and each
> prediction. But as shown here, that probability values are not consistent
> with prediction always so I was looking for decision_function method for
> random forest, but didn't find.
>
> Can anyone suggest me how can I get decision scores in case of random
> forest?
>
> thanks!
> Shalu
>
> On Thu, Jun 26, 2014 at 10:46 AM, Lars Buitinck <larsm...@gmail.com>
> wrote:
>
>> 2014-06-26 9:15 GMT+02:00 Andy <t3k...@gmail.com>:
>> > Maybe the calibration is not used for prediction? That would be a bit
>> > odd, though...
>>
>> That's exactly what's going on. Prediction is consistent with
>> decision_function, but not predict_proba.
>>
>>
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