Language-Classifier | Language classification using decision trees | Machine Learning library
kandi X-RAY | Language-Classifier Summary
kandi X-RAY | Language-Classifier Summary
Language classification using decision trees and adaboost.
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Top functions reviewed by kandi - BETA
- Runs the AdaModel
- Train the model
- Predict a given line
- Determine the value of the node
- Return a dictionary of features
- Return the letter pairs in a line
- Calculate the average length of a line
- Checks if the end of a line ends in the given string
- Run test
- Compute accuracy
- Parse filenames
- Runs test on examples
- Train a decision model
- Predict value for given line
- Use pickle
- Print usage information
Language-Classifier Key Features
Language-Classifier Examples and Code Snippets
Community Discussions
Trending Discussions on Language-Classifier
QUESTION
I want do add more examples to my classifier by java sdk. The idea is periodically increase the data size and improving the classifier. However, the docs only shows training option to create new classifier.
If I can't retrain, can I get the used data in the original classifier to train a new classifier by sdk?
In case of this features don't existing, what's the best approach? Increase my data locally and create new classifiers in each new train sending the whole database?
...ANSWER
Answered 2020-May-13 at 09:54There is no API to either update a classifier, nor to retrieve the training data of an existing classifier.
The corpora that the service generates do not have an update or reinforced learning option. Hence the need to generate a new corpus if there is a change in the training data.
Which also means that when the corpus is created there is no need for the service to keep the training data.
QUESTION
I'm new to machine learning and I'm doing my "hello world" using sklearn and nltk, but I have problems with the result of the prediction, it always throws me a single value.
I am following a tutorial that I obtained, that has errors and I have been modifying it little by little until in the end it gave me the result, but it is not the expected one.
Attach the tutorial link: https://towardsdatascience.com/text-classification-using-k-nearest-neighbors-46fa8a77acc5
I attach my current code: (always show: "Conditions" as final result)
...ANSWER
Answered 2019-Jun-11 at 20:25After printing out x_train and y_train, you'll figure out the bug.
For some reason, your Y is the feature while your X is your label. If you changed the line x_train, y_train = X, Y to x_train, y_train = Y, X, it would work.
QUESTION
IBM Watson Natural Language Classifier (NLC) limits the text values in the training set to 1024 characters: https://console.bluemix.net/docs/services/natural-language-classifier/using-your-data.html#training-limits .
However the trained model can then classify every text whose length is at most 2048 characters: https://console.bluemix.net/apidocs/natural-language-classifier#classify-a-phrase .
This difference creates some confusion for me: I have always known that we should apply the same pre-processing to both training phase and production phase, therefore if I had to cap off the training data at 1024 chars I would do the same also in production.
Is my reasoning correct or not? Should I cap off the text in production at 1024 chars (as I think I should) or at 2048 chars (maybe because 1024 chars are too few)?
Thank you in advance!
...ANSWER
Answered 2018-Nov-27 at 08:20Recently, I had the same question and one of the answers on an article clarified the same
Currently, the limits are set at 1024 for training and 2048 for testing/classification. The 1024 limit may require some curation of the training data prior to training. Most organizations who require larger character limits for their data end up chunking their input text into 1024 chunks. Additionally, in use cases with data similar to the Airbnb reviews, the primary category can typically be assessed within the first 2048 characters since there is often a lot of noise in lengthy reviews.
Here's the link to the article
QUESTION
I am trying to connect to the Watson Developer Cloud API, and am having issues installing Watson Developer Cloud. I am using
...ANSWER
Answered 2018-Apr-09 at 16:32Are you by chance running in a VM? If you are, try installing it on a stand alone (not VM) machine.
I had a similar issue trying to get Watson Developer going on a VM and it also stuck on the Twisted component - but i was able to take the (almost) same environment on a stand alone basis (not VM) and it installed.
I assumed that I had somehow not configured the interface correctly (which is probably true) - but it installed on the stand alone without issue.
QUESTION
I am trying to code a simple web app which calls IBM Watson NLC api to classify the text user will input to a textbox.
I have created the following and it works if single word is input and fails incase a sentence is given. Whats wrong? any help pls.
Input language will be arabic.
File 1 - NLCApp.php ...ANSWER
Answered 2018-Feb-11 at 10:34Please try this:
$ch = curl_init($url);- remove unused headers
- add error handling
QUESTION
I'm trying to invoke the deleteClassifier() method in the Natural Language Classifier service in IBM Watson platform, the operation completes but does not delete the classifier. Here's my code and the output:
Code:
...ANSWER
Answered 2017-Apr-30 at 20:12The deleteClassifier needs to be invoked using .execute()
Add .execute() to
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Install Language-Classifier
You can use Language-Classifier like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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