New answers tagged

1

The table of correlation coefficients shows the pairwise correlation between the variables in your data set: on a range from 0 (no correlation) to 1 (full correlation), to what extent does variation in one variable explain variation in the other variable? The coefficients from the regression table, on the other hand, describe the relation between y and ...


1

This blog article might be a good starting point. From what you described and depending on your data, semantic segmentation might be overkill and classification will suffice. Either way, the first step will be to get your hands on training data. If you do not have labels already, this might mean that you have to sit down for a while an label a bunch of ...


0

Parameters are set to default. This is not optimal : 1) Other parameters could give better results for a given model. 2) You can't conclude a model is better than another if you haven't set good parameters. 3) Without early stopping or penalty, you are likely to overfit. This is bad generally. In real life, the code you have won't really work. Generally ...


0

Suppose I take a part of the data as validation data, which contains whole blocks. If I split the remaining rows randomly in training and test data, the accuracy of the learned Random Forest is very high on training and test data but very low on the validation data. This is due to the fact that the Random Forest learns in this case to identify the individual ...


1

Experimentally: using cross-validation on a subset of your training data, compute the performance of every option that you want to consider. Then select the best option and train the final model using this option. // different settings for hyper-parameters, // for instance different pruning criteria: hpSet = { hp1, hp2, ...} trainSet, testSet = split(...


0

The ml model with single user data would be highly biased and if you try that model ( using features of only one person) on another person, will have poor accuracy. The more data you have, the less bias it has, it will fit better for predicting future values.


0

I want to build a deep learning model to predict the next job title when a current title is given. Are there any ways that I could achieve this using some deep learning model? I think that approaching this problem in the classification way (input:- current job embedding, output:- getting next job title as a class) can somewhat be time-consuming (not ...


1

In most cases, you would use a file-storage solution such as Amazon S3 or Google Cloud and many others, which provide designated solutions for large object storage and retrieval. You would then ideally want to update your code to retrieve the model directly from the file storage. Whether this download needs be done on every run or only once (storing the ...


1

On my phone right now but you should set the batch size to a specific size. The generator will keep passing batches back. Let's say your batch size is 32 but your almost out of sample and only 20 are left. The generator will pass back a batch that's partially full and that is fine. Someone else will have to answer the question on having each batch different ...


1

Ill supply two tutorials I used when I first started using fit_generator. That being said the first thing to remember is that a generator is essentially like any other function your write that returns something with the exception is that the function runs a continuous loop that is designed not to exit. For example, in a normal function, you would use return ...


0

LightGBM and XGBoost Libraries can handle missing values LightGBM: will ignore missing values during a split, then allocate them to whichever side reduces the loss the most XGBoost: the instance is classified into a default direction (the optimal default directions are learnt from the data somehow) Finally, it is NOT a general property of ...


0

Your problem can be considered as multiclass classification problem. So, you have a dataset of features X and the predictor y. Where X contain Income, age, sex,etc. and y is an item that one customer will buy with higher probability. To achieve your goal and predict the probability of a customer you can use any classifier from scikit-learn Library (if you ...


Top 50 recent answers are included