Community Digest

Top new questions this week:

feature importance after classification

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 ...

classification recurrent-neural-net  
asked by Rick0 6 votes
answered by 10xAI 5 votes

Bert for QuestionAnswering input exceeds 512

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 ...

bert transformer question-answering huggingface  
asked by Fei Yan 4 votes
answered by ncasas 6 votes

Neural Network regression negative performance

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 ...

neural-network regression predictive-modeling mlp  
asked by Mark 3 votes

When I add regularization like L1,L2 , do I need more epochs to properly train my model?

When I add regularization techniques in my model like L1 or L2 do i need more epochs to properly converge my model. ...

deep-learning regularization  
asked by Shiv 3 votes
answered by kate-melnykova 3 votes

Can i expect good results having low correlation attributes?

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 ...

machine-learning classification visualization correlation  
asked by joann2555 3 votes
answered by martin 3 votes

Mean of mean and average

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 ...

mean  
asked by Gulliver 2 votes
answered by Joseph Bloom 0 votes

Are "Gradient Boosting Machines (GBM)" and GBDT exactly the same thing?

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 ...

xgboost ensemble-modeling gbm ensemble-learning gradient  
asked by Tyler傲来国主 2 votes
answered by Carlos Mougan 2 votes

Greatest hits from previous weeks:

AttributeError: 'numpy.ndarray' object has no attribute 'columns'

...

scikit-learn pandas numpy  
asked by Balu 3 votes
answered by Djib2011 2 votes

When should I use Gini Impurity as opposed to Information Gain?

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?

machine-learning decision-trees  
asked by Krish Mahajan 87 votes
answered by Dawny33 61 votes

Adding Features To Time Series Model LSTM

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 ...

machine-learning neural-network deep-learning time-series  
asked by Rjay155 56 votes
answered by Adam Sypniewski 49 votes

GBM vs XGBOOST? Key differences?

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 ...

machine-learning algorithms xgboost ensemble-modeling gbm  
asked by Aman 53 votes
answered by Icyblade 49 votes

What is dimensionality reduction? What is the difference between feature selection and extraction?

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 ...

feature-selection feature-extraction dimensionality-reduction  
asked by alvas 66 votes
answered by damienfrancois 55 votes

train_test_split() error: Found input variables with inconsistent numbers of samples

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 ...

python scikit-learn sampling  
asked by josh_gray 33 votes
answered by tuomastik 25 votes

K-Means clustering for mixed numeric and categorical data

My data set contains a number of numeric attributes and one categorical. Say, NumericAttr1, NumericAttr2, ..., NumericAttrN, CategoricalAttr, where ...

data-mining clustering octave k-means categorical-data  
asked by IharS 162 votes
answered by Tim Goodman 151 votes

Can you answer these questions?

Abnormal value of ROC AUC score in Binary Classifier Model

I have developed the following model for a Binary Classifier. I have to evluate using roc_auc_score. I am getting unusual values for ...

python neural-network deep-learning keras python-3.x  
asked by Ishan Dutta 2 votes

Using categorical and continuous variables in Deep Learning

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 ...

neural-network deep-learning dataset representation  
asked by Lila 1 vote
answered by BeamsAdept 0 votes

Missing value Imputation in dataset

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 ...

machine-learning data-cleaning k-nn data-imputation  
asked by Bharathi A 1 vote
You're receiving this message because you subscribed to the Data Science community digest.
Unsubscribe from this community digest       Edit email settings       Leave feedback       Privacy
Stack Overflow

Stack Overflow, 110 William Street, 28th floor, New York, NY 10038

<3