This error is caused by the fact that you are passing a list of arrays with the image data to .fit() instead of a single array with the first dimension being the number of samples. Try using numpy.stack to convert the list of arrays to a single numpy array.
In essense all ML models are numerical algorithms which correlate numbers in various ways in order to arrive at other numbers.
So, backstage, all algorithms somehow use only numerical data, even if they do not force the programmer to do so from the start (like CARTs, for example).
Some models, eg all types of Neural Networks, require the programmer to ...
Why do you want to remove outliers? Do you think these are wrong data? Do you think they have outsized impact on the model? Are these rows the model should get right or wrong and you move to the validation set? Other reasons? Know why you want to identify outliers then choose the appropriate method. I think it is better to find what may be outliers then ...
As requested please find below two links that provides the dataset for English - Hausa language dataset
Hausa corpus dataset
Research paper: The first large scale collection of diverse Hausa language datasets
HugginFace for Hausa
Published Hausa Corpora
Library of Congress
Major websites for non-tweet-based collection
Description more formal texts from ...
If I understood correctly
You have data like this
So in fact you actually have many 0 labels, more than 1 labels, which are all the products a client didn't buy
If you want to predict the next product a client will ...
I finally solved this by using the Virusshare website. It has millions of malwares, and is free.
Note that around 1-2% of their PE files are probably benign, meaning less than 1-2 detection on VirusTotal, so just labeling every single PE file as malware might not be academically complete.
If you want to fix skewness the better alternative to a simple log transform is a Power Transformation. I think Box-Cox will not work with zeros, since it accepts only positive values, but Yeo-Johnson will.
If you have a lot of zeros it might be a good idea to check for zero variance if your data is continuous and near-zero variance if your data is discrete, ...
This task could be treated as a one-class classification problem, where a binary classifier is trained only with positive instances. Instead of determining the optimal limit between classes, the model tries to determine what characterizes the positive class, considering everything else as negative.
Yes you have to remove one of them. For example when you plot a heatmap and notice that 2 features A and B have a correlation value of 0.91, remove one of them as removing both of them will lead to information loss.
After removing one of them, again plot a heatmap of the remaining features and you'll notice the correlation values of other features have ...