I am trying to code a two class classification DT problem that I used SAS EM before. But trying to do it in Sklearn. The target variable is a two class categorical variable. But there are a few continuous independent variables. In SAS I could specify the "Maximum Number of Branches" for each split. So when it is set to 4, some leaf will split into 2 and some in 4 (especially for continuous variables). I could not find an equivalent parameter in sklearn. Looked at "max_leaf-nodes". But that controls the total number of "leaf" nodes of the entire tree. I am sure some of you probably has faced the same situation and already found a solution. Please help/share. I will really appreciate it.
I don't think this option is available in sklearn, You will find this Post very useful for your Classification DT; as it lists all the options you have available.
I would recommend creating Bins for your continues variables; this way you force the branches to be the number of bins you have.
Example: For continuous variable COl1 has values between 1-100; you can create a 4 bins 1-25, 26-50 , 51-75, 76-100. or you can create the bins bases on the median.
Related
I have a dataset with 10 variables and I am looking to reduce it to a single "score". I understand the basics of PCA, it uses the covariance matrix of the variables to create 10 eigenvectors and 10 eigenvalues. Normally what is done is people multiply the eigenvectors by the normalized data to generate principal components, they pick some arbitrary number of principal components, and throw them into a regression and get the fitted value. In other words, the coefficients multiplied by the principal components allow for a data reduction to a single variable.
My question is whether I need to do the regression step (I do not have a dependent variable). Instead of doing the regression, could I use the eigenvalues as my coefficients? In other words, could I take the inner product of the vector of eigenvalues and the principal component to create a single variable?
I've never seen it used this way (and as far as I'm aware no one has even asked this question), but it seems intuitive to me. Am I missing something or is this legit and I just haven't looked in the right places? Thanks!
Do you have a target variable?
Because there are two things here,
principal component regression and
principal components analysis.
If you have a target variable (a variable you want to predict or explain) the regression if not the second.
PC Regression --> you need a target variable
PC Analysis --> you do not need any regression step.
I think you are in the second case. So you just want to reduce your 10 variables into 1 that summarize everything.
Perhaps and interesting tutorial in Python: https://jakevdp.github.io/PythonDataScienceHandbook/05.09-principal-component-analysis.html
My question is, when building a decision tree in sklearn, if I have a categorical variable, is there a problem if I manually input the values of the variable as numbers? (assuming the dataframe is small)
And, will there be difference in results if my variable is nominal or ordinal?
I don't think there should be much difference since the theory says that you should look for the best combination in terms of entropy and other metrics, so it shouldn't care if one value is smaller than another.
Thank you very much
There are differences if your categorical variable is ordinal or nominal:
If your variable is ordinal, you can just change each categories for a number (for example: bad, normal, good can be changed for 1,2,3). Note that you are keeping only one column. You can do it manually if you have few samples. You can use LabelEncoder from sklearn to do it.
If your variable is not ordinal you have to add new columns to you dataset, one for each category. You can do it manually, but I would recommend use pd.get_dummies().
To sump up, you have to be very careful knowing if the categorical variable is ordinal or not. And you can deal with them manually (you would obtain same results), but it's recommend to use functions predefined to avoid some mistakes.
I have started using scikit-learn Decision Trees and so far it is working out quite well but one thing I need to do is retrieve the set of sample Y values for the leaf node, especially when running a prediction. That is given an input feature vector X, I want to know the set of corresponding Y values at the leaf node instead of just the regression value which is the mean (or median) of those values. Of course one would want the sample mean to have a small variance but I do want to extract the actual set of Y values and do some statistics/create a PDF. I have used code like this how to extract the decision rules from scikit-learn decision-tree?
To print the decision tree but the output of the 'value' is the single float representing the mean. I have a large dataset so limit the leaf size to e.g. 100, I want to access those 100 values...
another solution is to use an (undocumented?) feature of the sklearn DecisionTreeRegressor object which is .tree.impurity
it returns the standard deviation of the values per each leaf
For big datasets with 2bil+ samples and approximately 100+ features per sample. Among these, 10% features you have are numerical/continuous variables and the rest of it are categorical variables (position, languages, url etc...).
Let's use some examples:
e.g: dummy categorical feature
feature: Position
real values: SUD | CENTRE | NORTH
encoded values: 1 | 2 | 3
...would have sense use reduction like SVD because distance beetween sud:north > sud:centre and, moreover, it's possible to encode (e.g OneHotEncoder, StringIndexer) this variable because of the small cardinality of it values-set.
e.g: real categorical feature
feature: url
real values: very high cardinality
encoded values: ?????
1) In MLlibthe 90% of the model works just with numerical values (a part of Frequent Itemset and DecisionTree techniques)
2) Features transformers/reductor/extractor as PCA or SVD are not good for these kind of data, and there is no implementation of (e.g) MCA
a) Which could be your approach to engage with this kind of data in spark, or using Mllib?
b) Do you have any suggestions to cope with this much categorical values?
c) After reading a lot in literature, and counting the implemented model in spark, my idea, about make inference on one of that features using the others (categorical), the models at point 1 could be the best coiche. What do you think about it?
(to standardize a classical use case you can imagine the problem of infer the gender of a person using visited url and other categorical features).
Given that I am a newbie in regards to MLlib, may I ask you to provide a concrete example?
Thanks in advance
Well, first I would say stackoverflow works in a different way, you should be the one providing a working example with the problem you are facing and we help you out using this example.
Anyways I got intrigued with the use of the categorical values like the one you show as position. If this is a categorical value as you mention with 3 levels SUD,CENTRE, NORTH, there is no distance between them if they are truly categorical. In this sense I would create dummy variables like:
SUD_Cat CENTRE_Cat NORTH_Cat
SUD 1 0 0
CENTRE 0 1 0
NORTH 0 0 1
This is a truly dummy representation of a categorical variable.
On the other hand if you want to take that distance into account then you have to create another feature which takes this distance into account explicitly, but that is not a dummy representation.
If the problem you are facing is that after you wrote your categorical features as dummy variables (note that now all of them are numerical) you have very many features and you want to reduce your feature's space, then is a different problem.
As a rule of thumbs I try to utilize the entire feature space first, now a plus since in spark computing power allows you to run modelling tasks with big datasets, if it is too big then I would go for dimensionality reduction techniques, PCA etc...
I have a trading strategy on the foreign exchange market that I am attempting to improve upon.
I have a huge table (100k+ rows) that represent every possible trade in the market, the type of trade (buy or sell), the profit/loss after that trade closed, and 10 or so additional variables that represent various market measurements at the time of trade-opening.
I am trying to find out if any of these 10 variables are significantly related to the profits/losses.
For example, imagine that variable X ranges from 50 to -50.
The average value of X for a buy order is 25, and for a sell order is -25.
If most profitable buy orders have a value of X > 25, and most profitable sell orders have a value of X < -25 then I would consider the relationship of X-to-profit as significant.
I would like a good starting point for this. I have installed RapidMiner 5 in case someone can give me a specific recommendation for that.
A Decision Tree is perhaps the best place to begin.
The tree itself is a visual summary of feature importance ranking (or significant variables as phrased in the OP).
gives you a visual representation of the entire
classification/regression analysis (in the form of a binary tree),
which distinguishes it from any other analytical/statistical
technique that i am aware of;
decision tree algorithms require very little pre-processing on your data, no normalization, no rescaling, no conversion of discrete variables into integers (eg, Male/Female => 0/1); they can accept both categorical (discrete) and continuous variables, and many implementations can handle incomplete data (values missing from some of the rows in your data matrix); and
again, the tree itself is a visual summary of feature importance ranking
(ie, significant variables)--the most significant variable is the
root node, and is more significant than the two child nodes, which in
turn are more significant than their four combined children. "significance" here means the percent of variance explained (with respect to some response variable, aka 'target variable' or the thing
you are trying to predict). One proviso: from a visual inspection of
a decision tree you cannot distinguish variable significance from
among nodes of the same rank.
If you haven't used them before, here's how Decision Trees work: the algorithm will go through every variable (column) in your data and every value for each variable and split your data into two sub-sets based on each of those values. Which of these splits is actually chosen by the algorithm--i.e., what is the splitting criterion? The particular variable/value combination that "purifies" the data the most (i.e., maximizes the information gain) is chosen to split the data (that variable/value combination is usually indicated as the node's label). This simple heuristic is just performed recursively until the remaining data sub-sets are pure or further splitting doesn't increase the information gain.
What does this tell you about the "importance" of the variables in your data set? Well importance is indicated by proximity to the root node--i.e., hierarchical level or rank.
One suggestion: decision trees handle both categorical and discrete data usually without problem; however, in my experience, decision tree algorithms always perform better if the response variable (the variable you are trying to predict using all other variables) is discrete/categorical rather than continuous. It looks like yours is probably continuous, in which case in would consider discretizing it (unless doing so just causes the entire analysis to be meaningless). To do this, just bin your response variable values using parameters (bin size, bin number, and bin edges) meaningful w/r/t your problem domain--e.g., if your r/v is comprised of 'continuous values' from 1 to 100, you might sensibly bin them into 5 bins, 0-20, 21-40, 41-60, and so on.
For instance, from your Question, suppose one variable in your data is X and it has 5 values (10, 20, 25, 50, 100); suppose also that splitting your data on this variable with the third value (25) results in two nearly pure subsets--one low-value and one high-value. As long as this purity were higher than for the sub-sets obtained from splitting on the other values, the data would be split on that variable/value pair.
RapidMiner does indeed have a decision tree implementation, and it seems there are quite a few tutorials available on the Web (e.g., from YouTube, here and here). (Note, I have not used the decision tree module in R/M, nor have i used RapidMiner at all.)
The other set of techniques i would consider is usually grouped under the rubric Dimension Reduction. Feature Extraction and Feature Selection are two perhaps the most common terms after D/R. The most widely used is PCA, or principal-component analysis, which is based on an eigen-vector decomposition of the covariance matrix (derived from to your data matrix).
One direct result from this eigen-vector decomp is the fraction of variability in the data accounted for by each eigenvector. Just from this result, you can determine how many dimensions are required to explain, e.g., 95% of the variability in your data
If RapidMiner has PCA or another functionally similar dimension reduction technique, it's not obvious where to find it. I do know that RapidMiner has an R Extension, which of course let's you access R inside RapidMiner.R has plenty of PCA libraries (Packages). The ones i mention below are all available on CRAN, which means any of the PCA Packages there satisfy the minimum Package requirements for documentation and vignettes (code examples). I can recommend pcaPP (Robust PCA by Projection Pursuit).
In addition, i can recommend two excellent step-by-step tutorials on PCA. The first is from the NIST Engineering Statistics Handbook. The second is a tutorial for Independent Component Analysis (ICA) rather than PCA, but i mentioned it here because it's an excellent tutorial and the two techniques are used for the similar purposes.