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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 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 am working on a text classification problem. I have huge amount of data and when I am trying to fit data into the machine learning model it is causing a memory error. Is there any way through which I can fit data in parts to avoid memory error.
Additional information
I am using linearSVC model.
I have training data of 1.1 million rows.
I have vectorized text data using tfidf.
The shape of vectorized data (1121063, 4235687) which has to be
fitted into the model.
Or is there any other way out of this problem.
Unfortunately, I don't have any reproducible code for the same.
Thanks in advance.
The simple answer is not to use what I assume is the scikit-learn implementation of linearSVC and instead use some algorithm/implementation that allows training in batches. Most common of which are neural networks, but several other algorithms exists. In scikit-learn look for classifiers with the partial_fit method which will allow you to fit your classifier in batches. See e.g. this list
You could also try what's suggested from sklearn.svm import SVC (the second part, the first is using LinearSVC, which you did):
For large datasets consider using :class:'sklearn.svm.LinearSVC' or
:class:'sklearn.linear_model.SGDClassifier' instead, possibily after a :class:'sklearn.kernel_approximation.Nystroem' transformer.
If you check SGDClassifier() you can set the parameter "warm_start=True" so when you iterate trough your dataset it won't lose it's state.:
clf = SGDClassifier(warm_start=True)
for i in 'loop your data':
clf.fit(data[i])
Additionally you could reduce the dimension of your dataset by removing some words from your TFIDF model. Check the "max_df" and "min_df" parameters, they'll remove words with frequency higher than or lower than, can be a % or an unit.
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Probably I'm missing something obvious--when I detrend my timeseries target data my model preforms way better. That's great. However, I'm trying to forecast an entire cycle and the trend ~is~ important. Is there a way to reconstitute the trend with these better scores or am I shooting in the foot by removing the trend in the first place?
mean absolute error with trend intact are on order of 0.001-0.003, with trend removed the scores are around 0.0001
Please provide more information.
What kind of model do you use?
Can you give an example of the time series e.g. pd.Series(data=[100,110,120,130,140])?
Have you checked for overfitting, meaning your model performs good on your current dataset but once new data comes in it performs really poor.
Does your time series really have a trend, or does it more or less move sideways (plot-wise speaking)?
Also you can combine different models, for example a linear model model might be a good choice for simulating the trend. Once you implemented the linear trend model you can add another model which tries to predict where the linear trend model is wrong. So esentially you could add a random forest algorithm which predicts the residuals of the linear model.
After you got both models you can simly sum up the prediction of both models. The linear one for the general trend and the random forest which tries to predict seasonality.
You can also look into models which recognize seasonality by nature, such as ARIMA models for example.
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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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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