svm

Import GensimWord2VecVectorizer in your Flask Web app python file.

There's a dirty way to do this, I'll write about it till someone answers with a better idea. Here we go:

This does not necessarily have to do with Bootstrap.

You are implying width: 97% to .separator-breadcrumb, so it does not use all the available space.

In order to fix it, replace line:

Did you look at the probabilities versus the fitted values? You can read about how probability works with SVM here.

If you want to look at the performance you can use the library DescTools and the function Conf or with the library caret and the function confusionMatrix. (They provide the same output.)

Please, consider trying to follow the instruction from quarkus/faq.adoc at main · quarkusio/quarkus:

1. Native compilation

Native executable fails on macOS with error: unknown type name 'uint8_t'

Your macOS has the wrong *.h files compared to the OS and no gcc compilation will work. This can happen when you migrate from versions of the OS. See Cannot compile any C++ programs; error: unknown type name 'uint8_t'

The solution is to

• sudo mv /usr/local/include /usr/local/include.old
• Reinstall XCode for good measure
• (optional?) brew install llvm
• generally reinstall your brew dependencies with native compilation

The executable should work now.

The purpose of (the idea behind) renaming the include directory (from include to include.old) seems to be explained in the answer.

• Related question. May contain some possible solutions. Broken c++ std libraries on macOS High Sierra 10.13 - Stack Overflow.

Simply do this

So if you look at the source code for tensorly linked here you can see that the documentation for the function in question partial_tucker says:

The issue was caused by WORKDIR clause before copy instruction.

Fixed Dockerfile:

• Point is that you're using predict() rather than predict_proba()/decision_function() to define your y_hat. This means - considering that the threshold vector is defined by the number of distinct values in y_hat (see here for reference), that you'll have few thresholds per class only on which tpr and fpr are computed (which in turn implies that your curves are evaluated at few points only).

• Indeed, consider what the doc says to pass to y_scores in roc_curve(), either prob estimates or decision values. In the example from sklearn, decision values are used to compute the scores. Given that you're considering a RandomForestClassifier(), considering probability estimates in your y_hat should be the way to go.

• What's the point then of label-binarizing the output? The standard definition for ROC is in terms of binary classification. To pass to a multiclass problem, you have to convert your problem into binary by using OneVsAll approach, so that you'll have n_class number of ROC curves. (Observe, indeed, that as SVC() handles multiclass problems in a OvO fashion by default, in the example they had to force to use OvA by applying OneVsRestClassifier constructor; with a RandomForestClassifier you don't have such problem as that's inherently multiclass, see here for reference). In these terms, once you switch to predict_proba() you'll see there's no much sense in label binarizing predictions.