I'm using Azure Machine Learning Studio in order to predict a column using Two-Class Boosted Decision Tree and split data.
The diagram that I have assembled can be found here:
What I need is that I'd like to see the column in the dataset that affects and influences the prediction the most. In other words, the column that changes the prediction result more than the other columns in the dataset.
Sorry if this has been asked before, but I couldn't find a proper answer to this simple question.
As said before, Permutation Feature Importance do the trick. Attach the Permutation Feature Importance block do the train block, click on the output port, and select visualize to get results of the module. The figure above shows the list of features sorted in descending order of their permutation importance scores.
An advice: be careful when interpreting results of permutation score when you have high correlated features.
For more info, see:
https://standupdata.com/category/permutation-feature-importance/ https://gallery.cortanaintelligence.com/Experiment/Permutation-Feature-Importance-5
Most ML implementation for decision tree includes something called "feature importance" in its model. For example, Scikit Learn Decision Tree Classifier has an attribute that indicates the importance of each feature.
Azure ML implementation should be no exception. Please look at the below link Permutation Feature Importance.
Related
I am trying to create a decision tree based on some training data. I have never created a decision tree before, but have completed a few linear regression models. I have 3 questions:
With linear regression I find it fairly easy to plot graphs, fit models, group factor levels, check P statistics etc. in an iterative fashion until I end up with a good predictive model. I have no idea how to evaluate a decision tree. Is there a way to get a summary of the model, (for example, .summary() function in statsmodels)? Should this be an iterative process where I decide whether a factor is significant - if so how can I tell?
I have been very unsuccessful in visualising the decision tree. On the various different ways I have tried, the code seems to run without any errors, yet nothing appears / plots. The only thing I can do successfully is tree.export_text(model), which just states feature_1, feature_2, and so on. I don't know what any of the features actually are. Has anybody come across these difficulties with visualising / have a simple solution?
The confusion matrix that I have generated is as follows:
[[ 0 395]
[ 0 3319]]
i.e. the model is predicting all rows to the same outcome. Does anyone know why this might be?
Scikit-learn is a library designed to build predictive models, so there are no tests of significance, confidence intervals, etc. You can always build your own statistics, but this is a tedious process. In scikit-learn, you can eliminate features recursively using RFE, RFECV, etc. You can find a list of feature selection algorithms here. For the most part, these algorithms get rid off the least important feature in each loop according to feature_importances (where the importance of each feature is defined as its contribution to the reduction in entropy, gini, etc.).
The most straight forward way to visualize a tree is tree.plot_tree(). In particular, you should try passing the names of the features to feature_names. Please show us what you have tried so far if you want a more specific answer.
Try another criterion, set a higher max_depth, etc. Sometimes datasets have unidentifiable records. For example, two observations with the exact same values in all features, but different target labels. Is this the case in your dataset?
I am developing a model in which it predicts whether the employee retains its job or leave the company.
The features are as below
satisfaction_level
last_evaluation
number_projects
average_monthly_hours
time_spend_company
work_accident
promotion_last_5years
Department
salary
left (boolean)
During feature analysis, I came up with the two approaches and in both of them, I got different results for the features. as shown in the image
here
When I plot a heatmap it can be seen that satisfaction_level has a negative correlation with left.
On the other hand, if I just use pandas for analysis I got results something like this
In the above image, it can be seen that satisfaction_level is quite important in the analysis since employees with higher satisfaction_level retain the job.
While in the case of time_spend_company the heatmap shows it is important while on the other hand, the difference is not quite important in the second image.
Now I am confused about whether to take this as one of my features or not and which approach should I choose in order to choose features.
Some please help me with this.
BTW I am doing ML in scikit-learn and the data is taken from here.
Correlation between features have little to do with feature importance. Your heat map is correctly showing correlation.
In fact, in most of the cases when you talking about feature importance, you must provide context of a model that you are using. Different models may choose different features as important. Moreover many models assume that data comes from IID (Independent and identically distributed random variables), so correlation close to zero is desirable.
For example in sklearn learn regression to get estimation of feature importance you can examine coef_ parameter.
Is there any way to get the output of the Boosted Decision Tree module in ML Studio? To analyze the learned tree, like in Weka.
Update: visualization of decision trees is available now! Right-click on the output node of the "Train Model" module and select "Visualize".
My old answer:
I'm sorry; visualization of decision trees isn't available yet. (I really want it too! You can upvote this feature request at http://feedback.azure.com/forums/257792-machine-learning/suggestions/7419469-show-variable-importance-after-experiment-runs, but they are currently working on it.)
Just FYI, you can currently see what the model builds for linear algorithms by right-clicking on the "Train Model" module output node and selecting "Visualize". It will show the initial parameter values and the feature weights. But for non-linear algorithms like decision trees, that visibility is still forthcoming.
Yes, I don't know your structure but you should have your dataset and the algorithm going into a train model and put the results of the train model with your other half of the dataset (if you used split) into a score model. You can see the scored label and scored probabilities here when you press visualise
Your experiment should look a bit like this. Connect the boosted decision tree with the dataset to a train model, you can see the results in the score model
I'm using SVM to classify clinical images of patients belonging to two different groups (patients vs. controls). I use PCA to extract a vector of features from each image, but I'd like to add other clinical information (for example, the output value of a clinical exam) in order to include it in the classification process.
Is there a way to do this?
I didn't find exhaustive suggestions in literature.
Thanks in advance.
You could just append the new information at the end of each sample. Other approach that you could try is having two additional classifiers, one that you could train with the additional information and a third classifier that would take the output of the other two classifiers as input to product a final prediction.
The question is pretty old, I' post my answer though.
If you have to scale your values, make sure that the new values are scaled to the similar range of your values in PCA-vector.
If your PCA vectors of features have constant length, you just start enumerating your features from length+1 e.g. for SVM input (libsvm):
1 1:<PCAval1> ... N:<PCAvalN> N+1:<Clinical exam value 1> ...
I've made a test adding such general features for cell recognition and the accuracy raised.
This Guide describes how to use enumerator-features.
P.S.:
In my test I've isolated, and squeezed cells from microscope image to a matrix 16x16. Each pixel in this matrix was a feature - 256 features. Additionally I've added some features as original size, moments, etc.
I'm working on a data mining project and would like to mine this dataset Higher Education Enrolments for interesting patterns or knowledge. My problem is figuring out which technique would work best for the dataset.
I'm currently working on the dataset using RapidMiner 5.0 and I removed two columns (E550 - Reference year, E931 - Total Student EFTSL) from the data as they would not be relevant to the analysis. The rest of the attributes are nominal except StudentID (integer) which I have used as my id. I'm currently using classification on it (Naive Bayes) but would like to get the opinion of others, hopefully those who have had more experience in this area. Thanks.
The best technique depends on many factors: type/distribution of training and target attribute, domain, value range of attributes, etc. The best technique to use is the result of data analysis and understanding.
In this particular case, you should clarify which is the attribute to predict.
Unless you already know what you are looking for, and know about the quality of the data source, you should always start by trying various exploratory analysis:
look at some of the first and second order statistics of all the
variables
generate histograms of each variable, to get an idea of the empirical
distribution of each
take a look at pairwise scatter plots of variables that might have
dependency
try other visualization that you might think of
These would give you a rough idea about what kind of pattern might be present and might be discoverable given the noise level. Then depending on what kind of pattern you are interested in, you could start trying various unsupervised pattern learning methods such as, PCA/ICA/factor analysis, clustering, or supervised methods, such as regression, classification.