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How do I view a model produced using scikit-learn?


How do I view a model produced using scikit-learn?

By : cheeto8
Date : November 15 2020, 06:54 AM
fixed the issue. Will look into that further I am learning to use scikit-learn as an alternative to R/SAS EM to produce machine learning models. I can produce a logistic regression classifier and apply it to a test set but I cannot seem to determine how to view the regression formula? I understand that I cannot save out as a PMML and only use joblib or pickle dumps, but these are not very intuitive. , After training classifier
code :
from sklearn.linear_model import LogisticRegression
# generating some dataset
from hep_ml.commonutils import generate_sample
X, y = generate_sample(n_samples=1000, n_features=10)
trained_regressor = LogisticRegression().fit(X, y)
trained_regressor.coef_
array([[ 0.85468364,  1.09829236,  1.19397439,  0.89664885,  0.81402396,
         1.00528498,  1.11475434,  0.88583092,  0.708134  ,  0.76573151]])
scores = safe_sparse_dot(X, self.coef_.T, dense_output=True) + self.intercept_


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Multilabel model scores better than the same model with binary-labels in scikit-learn

Multilabel model scores better than the same model with binary-labels in scikit-learn


By : Harsha Vardhan
Date : March 29 2020, 07:55 AM
This might help you It seems like when you binarize labels, random forest can predict multiple labels at once, while predicting only the most probable label in the initial case. F1 score is sensitive to that.
UPD: I'm wrong. I've tested it and it my case it always returns only one label, but score is still bad.
How to load previously saved model and expand the model with new training data using scikit-learn

How to load previously saved model and expand the model with new training data using scikit-learn


By : ValiDoll
Date : March 29 2020, 07:55 AM
should help you out LogisticRegression uses internally the liblinear solver that does not support incremental fitting. Instead you could use SGDClassifier(loss='log') that as a partial_fit method that could be used for this although in practice. The other hyperparameters are different. Be careful to grid search their optimal value carefully. Read the SGDClassifier documentation for the meaning of those hyperparameters.
CountVectorizer does not support incremental fitting. You would have to reuse the vectorizer fitted on train set #1 to transform #2. That means that any token from set #2 not already seen in #1 will be completely ignored though. This might not be what you expect.
How to use a scikit learn model from C#

How to use a scikit learn model from C#


By : villet
Date : March 29 2020, 07:55 AM
this will help It is not possible to load sklearn models in C# directly (for my knowledge).
There is a language for the language-/tool-independent exchange of ML models called PMML. sklearn doesn't bring native support for PMML however. If you're lucky, your model/pipeline might be exportable to PMML using third party tools and loadable in C# using third party libraries.
Pip install scikit-learn fails in virtualenv on Debian - Raspberry PI - Failed to build scikit-learn

Pip install scikit-learn fails in virtualenv on Debian - Raspberry PI - Failed to build scikit-learn


By : user2856449
Date : March 29 2020, 07:55 AM
I wish this helpful for you phd's answer brought me on the right track so to say. piwheels was already installed but i found that the way i tried to install scikit-learn was wrong. And the Cython-package was missing too. So here is how it was solved:
activate the virtual environment installed Cython via: python3 -m pip install Cython installed scikit-learn via: python3 -m pip install scikit-learn
scikit-learn - Should I fit model with TF or TF-IDF?

scikit-learn - Should I fit model with TF or TF-IDF?


By : Dean Carr
Date : March 29 2020, 07:55 AM
wish helps you To make the answer clear one must first examine the definitions of the two models.
LDA is a probabilistic generative model that generates documents by sampling a topic for each word and then a word from the sampled topic. The generated document is represented as a bag of words.
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