I am experiencing an efficiency problem with my app and I wish to solve it with machine learning, using scikit-learn.The problem: A Monte Carlo simulation is run over $n$ iterations. The resultant data set that it produces is a $m$ element array $M$, with each element being an array $N$ of size $n$, where each $N$ is sorted. For each element in $M$, we select the middle element (at index n/2) and store it in the results array $R$. The sorting of $N$ takes a very long time, and I wish to bypass this completely by using machine learning to train a model to output $R$.What I want to do: I want to use machine learning to produce a model that takes in the input that would go into the Monte Carlo simulation but then output $R$. I plan on training the model using the input and result from the Monte Carlo simulation.For example, an entry in the dataset I would use to train the model with will look like this:25684, 300, 1000, 18, 12, R: [32511, 33564, 34786, 35765, ..., 100544, 102888]So if we graphed $R$ for $k$ different sets of input, for the $k+1$th input we should see a graph along the lines of the previous graphs.My question: What machine learning algorithm can identify patterns in a dataset such that for a given set of input parameters, it will produce an $R$ similar to the $R$ that the Monte Carlo simulation would produce? So in essence, how can I generate a model that, when given 25684, 300, 1000, 18, 12, it will output [32511, 33564, 34786, 35765, ..., 100544, 102888]?
Producing datasets from input using Machine Learning
machine learning;dataset;algorithms;monte carlo