I'm trying to understand the implementation of splitters of decision tree in scikit learn.But I have stuck the point where it start finding the best split.
Need help in understanding the algo that is going on in it.
Code that I need to understand in from line 352 (in this file [https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/tree/_splitter.pyx )which basically the heart of decision tree built
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As part of my bachelor-thesis I want to evaluate two Semantic Parsers on one common Dataset.
The two parsers are namely:
TRANX by Yin and Neubig: https://arxiv.org/abs/1810.02720 and
Seq2Action by Chen et al.: https://arxiv.org/abs/1809.00773
As I am relatively new to the topic I'm pretty much starting from scratch.
I don't know how to implement the parsers on my computer in order to run them on the ATIS or GeoQuery dataset.
It would be great if there is someone here who can help me with this problem as I have really no clue yet on how to approach this whole situation.
I have a binary classification problem where I have around 15 features. I have chosen these features using some other model. Now I want to perform Bayesian Logistic on these features. My target classes are highly imbalance(minority class is 0.001%) and I have around 6 million records. I want to build a model which can be trained nighty or weekend using Bayesian logistic.
Currently, I have divided the data into 15 parts and then I train my model on the first part and test on the last part then I am updating my priors using Interpolated method of pymc3 and rerun the model using the 2nd set of data. I am checking the accuracy and other metrics(ROC, f1-score) after each run.
Problems:
My score is not improving.
Am I using the right approch?
This process is taking too much time.
If someone can guide me with the right approach and code snippets it will be very helpful for me.
You can use variational inference. It is faster than sampling and produces almost similar results. pymc3 itself provides methods for VI, you can explore that.
I only know this part of question. If you can elaborate your problem a bit further, maybe.. I can help you.
I must participate in a research project regarding a deep learning application for classification. I have a huge dataset containing over 35000 features - these are good values, taken from laboratory.
The idea is that I should create a classifier that must tell, given a new input, if the data seems to be good or not. I must use deep learning with keras and tensor flow.
The problem is that the data is not classified. I will enter a new column with 1 for good and 0 for bad. Problem is, how can I find out if an entry is bad, given the fact that the whole training set is good?
I have thought about generating some garbage data but I don't know if this is a good idea - I don't even know how to generate it. Do you have any tips?
I would start with anamoly detection. You can first reduce features with f.e. an (stacked) autoencoder and then use local outlier factor from sklearn: https://scikit-learn.org/stable/modules/outlier_detection.html
The reason why you need to reduce features first is, is because your LOF will be much more stable.
I have a datasets of MFCC that I know is good. I know how to put a row vector into a machine learning algorithm. My question is how to do it with MFCC, as it is a matrix? For example, how would I put this inside a machine learning algorithm:?
http://archive.ics.uci.edu/ml/machine-learning-databases/00195/Test_Arabic_Digit.txt
Any algorithm will work. I am looking at a binary classifier, but will be looking into it more. Scikit seems like a good resource. For now I would just like to know how to input MFCC into an algorithm. Step by step would help me a lot! I have looked in a lot of places but have not found an answer.
Thank you
In python, you can easily flatten a matrix so it becomes in a vector,for example you can use numpy and numpy's flatten function ,additionally an idea that comes to my mind(it's just an idea may or may not work) is to use convolutions, convolutions work very well with 2d structures.
Is there any good tutorial that explains how to weight the samples during successive iterations of constructing the decision trees for a sample training set? I want to specifically how to the weights are assigned after the first decision tree is constructed.
Decision tree is designed using Information Gain as an anchor and I am wondering how is this affected due to the misclassifications in the previous iterations being weighted.
Any good tutorial / example is highly appreciated.
A Short Introduction to Boosting from Freund and Schapire supplies an example of the AdaBoost algorithm using Quinlan's C4.5 Decision Tree model.