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From what I've seen on Github while looking for open source projects is that people usually do both.
You can have a section where one loads the models and runs the inference, and another section where you let the user train the models from scratch using your code.
I recommend doing this since some people do not want to retrain the model, especially if it'...
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The choice is mostly about your specific task: what do you need/want to do?
Many-to-one (single values) models have lower error, on average, since the quality of outputs decreases the more further in time you're trying to predict. Many-to-one (multiple values) sometimes is required by the task though.
An alternative could be to employ a Many-to-one (single ...
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To answer your question no.
The term "imbalance" usually refers to classification problems. For your case, i.e. a regression problem you can only look at the distribution of your target variable.
If by "balance" you mean them having a uniform distribution, you could argue that they are, if fact imbalanced. However, I'd argue that this is not the problem ...
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Do you think I implemented the code in the right way?
Code is correct, but he is minimal possible.
The features (X) have low correlation with the labels (Y).
It's could be the biggest problem, features have to have correlation with labels.
Do you have any suggestion to enhance the accuracy?
Make transformation of labels(X). Preparing of labels its ...
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So, the direct answer here is clearly NO.
The answer comes from the definitions of classification and regression. In a classification task what a model predicts is the probability of an instance to belong to a class (e.g. 'image with clouds' vs 'image without clouds' ), in regression you are trying to predict continuous values (e.g. the level of '...
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count_vectorizer.vocabulary_.items() and count_vectorizer.vocabulary_ Inconsistent number of returns
For future readers: as Ben wrote in the comments, it is really hard in general that len(my_dict) != len(my_dict.items()). When these kind of strage behaviours happen, it is always a good practise to perform some routine checks:
Clean your environment from every variable, even better restart the kernel and then run again your code.
Check your code for ...
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The minimum and maximum values are just known limits that are parts of the formula that reshapes the distribution of the data, so if a value is higher than the previously known value the resulting feature scaling (Normaliaztion) will be still appropriate.
An alternative is z-scores if you don't feel like using minimum and maximum values.
x'= (x-x̄) / σ
...
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"I'm not sure is it reasonable?"
yes it is. The metric you refer to is known as the Jaccard index.
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Yes, it's called overfitting. Your model is beginning to memorize the training set, but not performing well on any validation or test set. If your question is why is this happening, I'd like to refer you to another answer I wrote explaining this phenomenon in more detail.
One interesting question that could be made is why is the performance on the cross-...
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Do I use the mean vector from my training set to center my testing set when dimension reducing for classification?: Yes.
Test set must not be combined with training set in any step of calculating the reduced dimension space. Characteristics of final space is determined by training set and test set just follows that i.e. the mean-adjusting step uses ...
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I would suggest you to use Deep Q or A2C (I personally use A2C). As a terminal state you can consider the state in which every tile has been visited once, except if you want to your agent wonder forever. I create OpenAI gym gridworlds so I can use some of their very useful wrappers (example TimeLimit wrapper in which an episode terminates when a certain ...
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Question 1:
To do so, I would use the Gensim wrapper of FastText because Gensim has a predict_output_word which does exactly what you want. Given a list of context words, it provides the most fitting words.
Question 2:
It is up to the user. FastText isn't inherently CBOW or Skipgram. See: https://fasttext.cc/docs/en/python-module.html
Question 3:
Yes, ...
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You’ve asked two questions:
1) Do you make decisions about model superiority based on training or testing performance?
2) Which model should you prefer?
I’ll answer both.
1) First, come over to Cross Validated (the Stack Exchange site for statistics and similar topics, with some overlap to this site) and check out what Frank Harrell has to say about ...
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In addition to the ncasas' answer, which is good in my opinion, I'd like to point out that ReLU is computationally inexpensive, in contrast to sigmoid activation functions. They require only an if / then comparison, while e.g. the logistic function requires exponentiation, addition, and division. This practical consideration makes ReLU's attractive, ...
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One simple interpretation is that adding random noise of course can't help, and enough of it will sink your model performance. The model might eventually figure it out with enough training time, the right settings, etc, but it's having to sift through so much noise to find the signal you hid.
You have relatively few instances (2000) of relatively complex ...
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Take a look at this research paper.
It mentions two methods, a Minhash Encoding technique and Gamma-Poisson Matrix Factorization technique for high cardinality categorical data.
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One option would be Huber Loss which could be setup to increase the weight for some types of errors compared to other errors.
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