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I have a question regarding the model.fit method and overfitting from the scikit learn library in Pandas
Does the generic sklearn method model.fit(x---, y--) returns the score after applying the model to the specified training data?
Also, it is overfitting when performance on the test data degrades as more training data is used to learn the model?
model.fit(X, y) doesn't explicitly give you the score, if you assign a variable to it, it stores all the artifacts, training parameters. You can get the score by using model.score(X, y).
Overfitting in simple words is increasing the variance in your model by which your model fails to generalize. There are ways to reduce overfitting like feature engineering, normalization, regularization, ensemble methods etc.
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I'm trying to build a classification model and my target is not binary. The correlations of my features against my target are all weak (mostly 0.1). I have preprocessed my data and applied the all the algorithms i used to it (the algorithms i used are svm, knn, naivebayes,logistic regression, decision tree,gradient boosting, random forest). I evaluated all of the models with sklearn metrics.accuracy_score just to know how good they perform on my data but all of them scored 0.1~0.2 . The target is productline column.
My questions
How could this happen?
How to tackle this issue?
Is there any other algorithm that could make better score?
What's the accuracy if you use a dummy classifier? The accuracy of the models you have tried should be at least equal to that of the dummy classifier.
"How could this happen?" If there's no relationship between the features and the target variable, the model isn't going to return good results.
I'm not sure about the details of your dataset, but you can try to 1) Get more data 2) Get more features 3) Do some feature engineering 4) Clean your dataset if you haven't, there might be outliers or wrong inputs affecting your results
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I have written a ML-based Intrusion prediction. In the learning process, I used training and test data both labeled to evaluate the accuracy and generate confusion matrixes. I came up with good accuracy and now I want to test it with new data( Unlabeled data). How do I do that?
Okay so say you do test on unlabeled data and your algorithm predicts some X output. How can you check the accuracy, how can you check if this is correct or not? This is the only thing that matters in predictions, how your program works on data it has not seen before.
The short answer is, you can't. You need to split your data into:
Training 70%
Validation 10%
Test 20%
All of these should be labled and accuracy, confusion matrix, f measure and anything else should be computed on the labled test data that your program has not seen before. Your train on training data and every once in a while you check the performance on the validation data to see if it is doing well or if you need to do adjustments. In the very end you check on test data. This is supervised learning, you always need labeled data.
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How to determine what's the optimal number of iterations in learning a neural network?
One way of doing it is to split your training data into a train and validation set. During training, the error on the training set should decrease steadily. The error on the validation set will decrease and at some point start to increase again. At this point the net starts to overfit to the training data. What that means is that the model adapts to the random variations in the data rather than learning the true regularities. You should retain the model with overall lowest validation error. This is called Early Stopping.
Alternatively, you can use Dropout. With a high enough Dropout probability, you can essentially train for as long as you want, and overfitting will not be a significant issue.
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I have a python Machine Learning script which learns from Firewall log data. I am using Random Forest Classifier in it. If I run it as a service is it possible to train the model, again and again, each day whenever last day's Firewall logs are received.
These are the estimators in sklearn that support what you want to do and unfortunately for you Random Forest isn't one of them, so you will have to refit every time you add data.
If you insist on sticking with Random Forest, one option would be to reduce the number of features (based on the classifier you currently have) to increase the speed of refitting your classifier.
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Could you please explain what the "fit" method in scikit-learn does? Why is it useful?
In a nutshell: fitting is equal to training. Then, after it is trained, the model can be used to make predictions, usually with a .predict() method call.
To elaborate: Fitting your model to (i.e. using the .fit() method on) the training data is essentially the training part of the modeling process. It finds the coefficients for the equation specified via the algorithm being used (take for example umutto's linear regression example, above).
Then, for a classifier, you can classify incoming data points (from a test set, or otherwise) using the predict method. Or, in the case of regression, your model will interpolate/extrapolate when predict is used on incoming data points.
It also should be noted that sometimes the "fit" nomenclature is used for non-machine-learning methods, such as scalers and other preprocessing steps. In this case, you are merely "applying" the specified function to your data, as in the case with a min-max scaler, TF-IDF, or other transformation.
Note: here are a couple of references...
fit method in python sklearn
http://scikit-learn.org/stable/tutorial/basic/tutorial.html