Top new questions this week:
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I have time series data and more or less 200 features for each sample, I used a recurrent neural network for the binary classification task.
After the classification I would like to know which ...
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I'm training Bert on question answering (in Spanish) and i have a large context, only the context exceeds 512, the total question + context is 10k, i found that longformer is bert like for long ...
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I have a problem with the performance of a multi layer perceptron regressor (neural network) and I cannot figure out why.
Task: I am trying to improve a time series prediction. I have predictions of a ...
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When I add regularization techniques in my model like L1 or L2 do i need more epochs to properly converge my model.
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This was a question i saw in an interview for a data scientist position:
"Here is the following correlation heatmap that i got from my attributes. Regarding the correlation of each feature with ...
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In order to establish an overall rating for a product from a series of user ratings (from 1 to 5), I thought that the median would be a good idea so that extreme values would not have too much ...
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In the category of Gradient Boosting, I find some terms confusing.
I'm aware that XGBoost includes some optimization in comparison to conventional Gradient Boosting.
But are Gradient Boosting ...
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Greatest hits from previous weeks:
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Can someone practically explain the rationale behind Gini impurity vs Information gain (based on Entropy)?
Which metric is better to use in different scenarios while using decision trees?
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have been reading up a bit on LSTM's and their use for time series and its been interesting but difficult at the same time. One thing I have had difficulties with understanding is the approach to ...
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I am trying to understand the key differences between GBM and XGBOOST. I tried to google it, but could not find any good answers explaining the differences between the two algorithms and why xgboost ...
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From wikipedia,
dimensionality reduction or dimension reduction is the process of
reducing the number of random variables under consideration, and
can be divided into feature selection and ...
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Fairly new to Python but building out my first RF model based on some classification data. I've converted all of the labels into int64 numerical data and loaded into X and Y as a numpy array, but I am ...
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My data set contains a number of numeric attributes and one categorical.
Say, NumericAttr1, NumericAttr2, ..., NumericAttrN, CategoricalAttr,
where ...
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Can you answer these questions?
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I have developed the following model for a Binary Classifier. I have to evluate using roc_auc_score. I am getting unusual values for ...
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I want to apply a MLP to some business seller data. I found that the data is a mix of both categorical and continuous features. For what I read it is not advisable to feed a neural network with both ...
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I have two separate files for Testing and Training.
In the training data, I am dropping rows that contain too many missing values .
But , In the test data , I cannot afford to drop the rows so I have ...
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