LSTM with normally distributed data - machine-learning

I am running an lstm model. My data has a predictor variable which is normally distributed.
I was just wondering using LSTM on such distribution is a good idea?
Thanks.

Normally distributed is pretty much symmetrical about its mean. For that reason, using Gaussian Naive Bayes would be better to choose.

Related

CoreML on-device model training with tabular data

I'm trying to build an app that makes suggestions (distinct classes) based on a table with 4 features: latitude, longitude, time and weekday.
The training data of my app is 100% personal, so it doesn't really make sense to pre-train the model. I wanna be able to train on device. I know CoreML 3 supports updating for neural networks and kNN classifiers, but does this really help me with my tabular data?
Other tabular classifiers like boasted tree, random forest... can't be trained on device unfortunately. Are there alternatives to CoreML for on device training of those simpler machine learning algorithms? Or can CoreML somehow already do what I want.
Unfortunately I'm not really an expert in neural networks.
Just because Core ML doesn't provide something, doesn't mean it's impossible. :-) You can use existing libraries or implement the algorithm by yourself.
If you're looking to build a logistic regression classifier, this is fairly easy to implement by hand. (You can even use a neural network with a single layer for this and still use Core ML.)

Classifiers of machine learning for different parameters of dataset

Why behaviour of different classifier differ for different data?
Based on what parameters we can decide the good classifier for particular dataset?
For some dataset naive bayes gives better accuracy than SVM classifier
and for other dataset SVM performs better than naive bayes. Why is it
so? What is the reason?
Those are completely different classifiers. If you would have one classifier which is allways better than the other one. Why would you need the "bad one" then?
First google hit about when SVM's are not the best choice:
https://www.quora.com/For-what-kind-of-classification-problems-is-SVM-a-bad-approach
There is no general answer for this question. To understand which classifier to be used when you will need to understand the algorithm behind the classification procedure.
For instance logistic regression assumes a normal distribution of y and is generally useful when a particular parameter is not a uniquely deciding factor however combined weightage of the factors make a difference, for instance in text classification.
Decision tree on the other hand splits on the basis of parameter which gives most information. So if you have a set of parameters which is highly correlated with the label, then it makes more sense to use decision tree based classifiers.
SVM, work based on identifying adequate hyperplanes. These are generally useful when it is not possible to classify data in one plane but projecting them into higher plane classifies them easily. This is a nice tutorial on SVM https://blog.statsbot.co/support-vector-machines-tutorial-c1618e635e93
In short the only way to learn which classifier will be better in which situation is to understand how they work, and then figure out if they are best for your situation.
Another, crude way will be try every classifier and pick the best one, but i don't think you are interested in that.

held-out data and k-fold cross validation

I have learnt about holding some data out of the training set (development data for tuning model's parameters) and also k-fold cross validation. I got a question about them, Can we use them in all of the machine learning algorithms such as Decision Tree and Naïve Bayes?or there is restriction in using them? Is it better to use them in Decision Tree and Naïve Bayes rather than their pure algorithms in order to gain better results?
Any help would be highly appreciated.
Yes, you could use them in all machine learning algorithms. The approach is to test how best your learning algorithm can generalize and perform on an unseen dataset.

Machine learning algorithm for few samples and features

I am intended to do a yes/no classifier. The problem is that the data does not come from me, so I have to work with what I have been given. I have around 150 samples, each sample contains 3 features, these features are continuous numeric variables. I know the dataset is quite small. I would like to make you two questions:
A) What would be the best machine learning algorithm for this? SVM? a neural network? All that I have read seems to require a big dataset.
B)I could make the dataset a little bit bigger by adding some samples that do not contain all the features, only one or two. I have read that you can use sparse vectors in this case, is this possible with every machine learning algorithm? (I have seen them in SVM)
Thanks a lot for your help!!!
My recommendation is to use a simple and straightforward algorithm, like decision tree or logistic regression, although, the ones you refer to should work equally well.
The dataset size shouldn't be a problem, given that you have far more samples than variables. But having more data always helps.
Naive Bayes is a good choice for a situation when there are few training examples. When compared to logistic regression, it was shown by Ng and Jordan that Naive Bayes converges towards its optimum performance faster with fewer training examples. (See section 4 of this book chapter.) Informally speaking, Naive Bayes models a joint probability distribution that performs better in this situation.
Do not use a decision tree in this situation. Decision trees have a tendency to overfit, a problem that is exacerbated when you have little training data.

What classifier to use while performing unsupervised learning

I am new to Machine learning and I have this basic question. As I am weak in Math part of the algorithm I find it difficult to understand this.
When you are given a task to design a classifier(keep it simple -- a 2 class classifier) using unsupervised learning(no training samples), how to decide what type of classifier(linear or non-linear) to use? If we do not know this, then the importance on feature selection(which means indirectly knowing what the data set is) becomes very critical.
Am I thinking in the right direction or is there something big that I dont know. Insight into this topic is greatly appreciated.
classification is by definition a "supervised learning" problem. such models require examples of points within given classes to understand how to separate the classes from one another. if you are simply looking for relationships between unlabeled data points, you're solving an unsupervised problem. look into clustering algorithms. k-means is where a lot of people start.
hope this helps!
This is a huge problem. Yes, the term "clustering" is the best entry point for googling about that, but I understand that you want to train a classifier, where "training" means optimizing an objective function with parameters. The first choice is definitely not discriminative classifiers (such as linear ones), because with them, the standard maximum likelihood (ML) objective does not work without labels. If you absolutely want to use linear classifiers, then you have to tweak the ML objective, or better use another objective (approximating the classifier risk). But an easier choice is to rather look at generative models, such as HMMs, Naive Bayes, Latent Dirichlet Allocation, ... for which the ML objective works without labels.

Resources