I created a k-means clustering for clustering data based on 1 multidimentional feature i.e. 24-hour power usage by customer for many customers, but I'd like to figure out a good way to take data which hypothetically comes from matches played within a game for a player and tries to predict the win probability.
It would be something like:
Player A
Match 1
Match 2
.
.
.
Match N
And each match would have stats of differing dimensions for that player such as the player's X/Y coordinates at a given time, time a score was made by the player, and such. Example, the X/Y would have data points based on the match length, while scores could be anywhere between 0 and X, while other values might only have 1 dimension such as difference in skill ranking for the match.
I want to take all of the matches of the player and cluster them based on the features.
My idea to approach this is to cluster each multi-dimensional feature of the matches to summarize them into a cluster, then represent that entire feature for the match with a cluster number.
I would repeat this process for all of the features which are multi-dimensional until the row for each match is a vector of scalar values and then run one last cluster on this summarized view to try to see if wins and losses end up in distinctive clusters, and based on the similarity of the current game being played with the clustered match data, calculate the similarity to other clusters and assign a probability on whether it is likely going to become a win or a loss.
This seems like a decent approach, but there are a few problems that make me want to see if there is a better way
One of the key issues I'm seeing is that building model seems very slow - I'd want to run PCA and calculate the best number of components to use for each feature for each player, and also run a separate calculation to determine the best number of clusters to assign for each feature/player when I am clustering those individual features. I think hypothetically scaling this out over thousands to millions of players with trillions of matches would take an extremely long time to do this computation as well as update the model with new data, features, and/or players.
So my question to all of you ML engineers/data scientists is how is my approach to this problem?
Would you use the same method and just allocate a ton of hardware to build the model quickly, or is there some better/more efficient method which I've missed in order to cluster this type of data?
It is a completely random approach.
Just calling a bunch of functions just because you've used them once and they sound cool never was a good idea.
Instead , you first should formalize your problem. What are you trying to do?
You appear to want to predict wins vs. losses. That is classification not clustering. Secondly, k-means minimizes the sum-of-squares. Does it actually !ake sense to minimize this on your data? I doubt so. Last, you begin to be concerned about scaling something to huge data, which does not even work yet...
Related
I'm new to machine learning, and I understand that there are parameters and choices that apply to the model you attach to a certain set of inputs, which can be tuned/optimised, but those inputs obviously tie back to fields you generated by slicing and dicing whatever source data you had in a way that makes sense to you. But what if the way you decided to model and cut up your source data, and therefore training data, isn't optimal? Are there ways or tools that extend the power of machine learning into, not only the model, but the way training data was created in the first place?
Say you're analysing the accelerometer, GPS, heartrate and surrounding topography data of someone moving. You want to try determine where this person is likely to become exhausted and stop, assuming they'll continue moving in a straight line based on their trajectory, and that going up any hill will increase heartrate to some point where they must stop. If they're running or walking modifies these things obviously.
So you cut up your data, and feel free to correct how you'd do this, but it's less relevant to the main question:
Slice up raw accelerometer data along X, Y, Z axis for the past A number of seconds into B number of slices to try and profile it, probably applying a CNN to it, to determine if running or walking
Cut up the recent C seconds of raw GPS data into a sequence of D (Lat, Long) pairs, each pair representing the average of E seconds of raw data
Based on the previous sequence, determine speed and trajectory, and determine the upcoming slope, by slicing the next F distance (or seconds, another option to determine, of G) into H number of slices, profiling each, etc...
You get the idea. How do you effectively determine A through H, some of which would completely change the number and behaviour of model inputs? I want to take out any bias I may have about what's right, and let it determine end-to-end. Are there practical solutions to this? Each time it changes the parameters of data creation, go back, re-generate the training data, feed it into the model, train it, tune it, over and over again until you get the best result.
What you call your bias is actually the greatest strength you have. You can include your knowledge of the system. Machine learning, including glorious deep learning is, to put it bluntly, stupid. Although it can figure out features for you, interpretation of these will be difficult.
Also, especially deep learning, has great capacity to memorise (not learn!) patterns, making it easy to overfit to training data. Making machine learning models that generalise well in real world is tough.
In most successful approaches (check against Master Kagglers) people create features. In your case I'd probably want to calculate magnitude and vector of the force. Depending on the type of scenario, I might transform (Lat, Long) into distance from specific point (say, point of origin / activation, or established every 1 minute) or maybe use different coordinate system.
Since your data in time series, I'd probably use something well suited for time series modelling that you can understand and troubleshoot. CNN and such are typically your last resort in majority of cases.
If you really would like to automate it, check e.g. Auto Keras or ludwig. When it comes to learning which features matter most, I'd recommend going with gradient boosting (GBDT).
I'd recommend reading this article from AirBnB that takes deeper dive into journey of building such systems and feature engineering.
I am working on final year project which has to be coded using unsupervised learning (KMeans Algorithm). It is to predict a suitable game from various games regarding their cognitive skills levels. The skills are concentration, Response time, memorizing and attention.
The first problem is I cannot find a proper dataset that contains the skills and games. Then I am not sure about how to find out clusters. Is there any possible ways to find out a proper dataset and how to cluster them?
Furthermore, how can I do it without a dataset (Without using reinforcement learning)?
Thanks in advance
First of all, I am kind of confused with your question. But I will try to answer with the best of my abilities. K-means clustering is an unsupervised clustering method based on the distance (typically Euclidean) of data from each other. Data points with similar features will have a closer distance, and will then be clustered into the same cluster.
I assume you are trying build an algorithm that outputs a recommended game, given an individuals concentration, response time, memorization, and attention skills.
The first problem is I cannot find a proper dataset that contains the skills and games.
For the data set, you can literally build your own that looks like this:
labels = [game]
features = [concentration, response time, memorization, attention]
Labels is a n by 1 vector, where n is the number of games. Features is a n by 4 vector, and each skill can have a range of 1 - 5, 5 being the highest. Then populate it with your favorite classic games.
For example, Tetris can be your first game, and you add it to your data set like this:
label = [Tetris]
features = [5, 2, 1, 4]
You need a lot of concentration and attention in tetris, but you don't need good response time because the blocks are slow and you don't need to memorize anything.
Then I am not sure about how to find out clusters.
You first have to determine which distance you want to use, e.g. Manhattan, Euclidean, etc. Then you need to decide on the number of clusters. The k-means algorithm is very simple, just watch the following video to learn it: https://www.youtube.com/watch?v=_aWzGGNrcic
Furthermore, how can I do it without a dataset (Without using reinforcement learning)?
This question makes 0 sense because first of all, if you have no data, how can you cluster them? Imagine your friends asking you to separate all the green apples and red apples apart. But they never gave you any apples... How can you possibly cluster them? It is impossible.
Second, I'm not sure what you mean by reinforcement learning in this case. Reinforcement learning is about an agent existing in an environment, and learning how to behave optimally in this environment to maximize its internal reward. For example, a human going into a casino and trying to make the most money. It has nothing to do with data sets.
I'm working on a project where I need to predict future stats based on past stats of basketball players. I would like to be able to predict next season's statistics based on the statistics of the past three seasons (if there are three previous seasons to choose from). Does anyone have a suggestion for a good prediction algorithm I could use? The data is continuous and there can be anywhere between 5-14 dimensions (age, minutes, points, etc.)
Thanks!
Note: I'd really like to use the program Weka to do this.
Out of the box, random forest would likely give you a strong baseline, so I would start with this.
You can also try try linear regression, which is a simple yet relative effective method, but depending on the data might require a bit more tweaking (for example transforming some of the input and/or out variables).
Gradient boosting regression is another strong predictor, but typically also needs more tweaking to work well.
All of these algorithms have Weka implementations.
There obviously isn't one correct answer, but for anyone looking to do something similar, I'll better describe my problem and the solution that I've found. I created a csv file where each row is a different season, and each column contains a different attribute. For each attribute that I would like to predict, I have the stats for the current season and then another column for the stats for the previous season. The first (rookie) season will have 0 for all 'previous season' columns. With this data set, I loaded it into Weka and used a Multilayer Perceptron with the test-option set to Cross-Validation. I set the number of folds to somewhere between 80-90% of the number of seasons available.
Finally, to predict the next season's statistics, you add one more row to the end and input the last-season values with "?" in the columns that you would like to predict. If anyone would like a deeper example, I'd be glad to provide one.
I think also if you truly want to create an accurate prediction you have to look at player movement and if a player moves to a team with a losing record, do they increase their minutes to have a larger role which would inflate stats or move to a winning team for a lesser role where they could see a decrease in stats.
I was trying to cluster some documents using the KMeansClustering approach and successfully created the clusters. I saved the cluster id corresponding to a particular document for recommendations. So whenever I wanted to recommend documents similar to a particular document, I would query all the documents in a particular cluster and return n random documents from the cluster. However, returning any random document from the cluster did not seem appropriate and I read somewhere that we should be returning the documents nearest to the document in question.
So I started searching for calculating distance between documents and stumbled upon the RowSimilarity approach which returns 10 most similar documents to each document, ordered by distance. Now this approach relies on a similarity metric like LogLikelihood etc to calculate the distance between documents.
Now my question is this. How is clustering better/worse than RowSimilarity given that both the approaches use a similarity distance metric to calculate the distance between documents?
What I'm trying to achieve is that I'm trying to cluster products on the basis of their titles and other text properties to recommend similar products. Any help is appreciated.
Clustering is not just another variant of classification or recommendation. It is a different discipline.
When you are doing cluster analysis, you want to discover structure in the data. But then, you should actually be analyzing the structure you found.
Now k-means is not really meant for documents. It tries to find a near optimal partitioning of a data set into k Voronoi cells. Unless you have a good reason to believe that Voronoi cells are a good partitioning for your data, the algorithm may be pretty much useless. Just because it returns a result does not at all indicate that the result is useful.
For documents, Euclidean distance (and k-means is in fact optimizing Euclidean distances) are usually pretty much meaningless. The vectors are very sparse, and k-means cluster centers will then often resemble impossible (and thus insensible) "average documents".
And I havn't started on the need to find an appropriate value of k, on the Mahout implementation likely just being an approximation of Lloyds k-means approximation, and so on. Did you even check the cluster sizes? In situations like these, k-means will often produce degenerate results. For example, almost all clusters containing 1 or 0 elements, and a mega-cluster containing the rest. In this situation, you might in fact be returning just random documents from your database...
Just because you can use it does not mean it is helpful. Make sure to validate the individual steps of your approach, for example if the clusters are in any way useful and sensible!
Similarity is not the same thing as distance -- one is big when the other is small. Clustering is not the same as computing distances either. First you should decide whether you have a clustering problem -- it does not sound like you do based on what you say. So, don't use k-means.
What does dimensionality reduction mean exactly?
I searched for its meaning, I just found that it means the transformation of raw data into a more useful form. So what is the benefit of having data in useful form, I mean how can I use it in a practical life (application)?
Dimensionality Reduction is about converting data of very high dimensionality into data of much lower dimensionality such that each of the lower dimensions convey much more information.
This is typically done while solving machine learning problems to get better features for a classification or regression task.
Heres a contrived example - Suppose you have a list of 100 movies and 1000 people and for each person, you know whether they like or dislike each of the 100 movies. So for each instance (which in this case means each person) you have a binary vector of length 100 [position i is 0 if that person dislikes the i'th movie, 1 otherwise ].
You can perform your machine learning task on these vectors directly.. but instead you could decide upon 5 genres of movies and using the data you already have, figure out whether the person likes or dislikes the entire genre and, in this way reduce your data from a vector of size 100 into a vector of size 5 [position i is 1 if the person likes genre i]
The vector of length 5 can be thought of as a good representative of the vector of length 100 because most people might be liking movies only in their preferred genres.
However its not going to be an exact representative because there might be cases where a person hates all movies of a genre except one.
The point is, that the reduced vector conveys most of the information in the larger one while consuming a lot less space and being faster to compute with.
You're question is a little vague, but there's an interesting statistical technique that may be what you're thinking off called Principal Component Analysis which does something similar (and incidentally plotting the results from which was my first real world programming task)
It's a neat, but clever technique which is remarkably widely applicable. I applied it to similarities between protein amino acid sequences, but I've seen it used for analysis everything from relationships between bacteria to malt whisky.
Consider a graph of some attributes of a collection of things where one has two independent variables - to analyse the relationship on these one obviously plots on two dimensions and you might see a scatter of points. if you've three variable you can use a 3D graph, but after that one starts to run out of dimensions.
In PCA one might have dozens or even a hundred or more independent factors, all of which need to be plotted on perpendicular axis. Using PCA one does this, then analyses the resultant multidimensional graph to find the set of two or three axis within the graph which contain the largest amount of information. For example the first Principal Coordinate will be a composite axis (i.e. at some angle through n-dimensional space) which has the most information when the points are plotted along it. The second axis is perpendicular to this (remember this is n-dimensional space, so there's a lot of perpendiculars) which contains the second largest amount of information etc.
Plotting the resultant graph in 2D or 3D will typically give you a visualization of the data which contains a significant amount of the information in the original dataset. It's usual for the technique to be considered valid to be looking for a representation that contains around 70% of the original data - enough to visualize relationships with some confidence that would otherwise not be apparent in the raw statistics. Notice that the technique requires that all factors have the same weight, but given that it's an extremely widely applicable method that deserves to be more widely know and is available in most statistical packages (I did my work on an ICL 2700 in 1980 - which is about as powerful as an iPhone)
http://en.wikipedia.org/wiki/Dimension_reduction
maybe you have heard of PCA (principle component analysis), which is a Dimension reduction algorithm.
Others include LDA, matrix factorization based methods, etc.
Here's a simple example. You have a lot of text files and each file consists some words. There files can be classified into two categories. You want to visualize a file as a point in a 2D/3D space so that you can see the distribution clearly. So you need to do dimension reduction to transfer a file containing a lot of words into only 2 or 3 dimensions.
The dimensionality of a measurement of something, is the number of numbers required to describe it. So for example the number of numbers needed to describe the location of a point in space will be 3 (x,y and z).
Now lets consider the location of a train along a long but winding track through the mountains. At first glance this may appear to be a 3 dimensional problem, requiring a longitude, latitude and height measurement to specify. But this 3 dimensions can be reduced to one if you just take the distance travelled along the track from the start instead.
If you were given the task of using a neural network or some statistical technique to predict how far a train could get given a certain quantity of fuel, then it will be far easier to work with the 1 dimensional data than the 3 dimensional version.
It's a technique of data mining. Its main benefit is that it allows you to produce a visual representation of many-dimensional data. The human brain is peerless at spotting and analyzing patterns in visual data, but can process a maximum of three dimensions (four if you use time, i.e. animated displays) - so any data with more than 3 dimensions needs to somehow compressed down to 3 (or 2, since plotting data in 3D can often be technically difficult).
BTW, a very simple form of dimensionality reduction is the use of color to represent an additional dimension, for example in heat maps.
Suppose you're building a database of information about a large collection of adult human beings. It's also going to be quite detailed. So we could say that the database is going to have large dimensions.
AAMOF each database record will actually include a measure of the person's IQ and shoe size. Now let's pretend that these two characteristics are quite highly correlated. Compared to IQs shoe sizes may be easy to measure and we want to populate the database with useful data as quickly as possible. One thing we could do would be to forge ahead and record shoe sizes for new database records, postponing the task of collecting IQ data for later. We would still be able to estimate IQs using shoe sizes because the two measures are correlated.
We would be using a very simple form of practical dimension reduction by leaving IQ out of records initially. Principal components analysis, various forms of factor analysis and other methods are extensions of this simple idea.