Hi, I am using 4 different classifiers of Random Forest, SVM, Decision Tree and Neural Network on different datasets in one of the datasets all of the classifiers are giving 100% accuracy which I do not understand why and in other datasets these algorithms are giving above 90% accuracies. Random forest performs best in all datasets. Could anyone please suggest how can I make sure if my algorithms are not overfitting? if yes, then how can I overcome that? Regards
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Hi Doug, Thanks for your reply. I am getting your points. I am using impute before anything gets applied. I will apply your thoughts and will be sharing results with you. Regards
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Hi Doug, I am using Neural Network, SVM and Linear Regression. for easier understanding, I have got this data after reading static information from files, each file contains multiple records called sections. each section has a type, address, size etc. There are in total 70 unique sections. But not a single file contains sections more than 20 sections (records). Some files have 10 sections, some 15 and some files have 3 sections. In my views these are not missing values. But I am not sure whether this difference of sections is classified as a missingness or a normal thing. As in my views for example every person in real life situation has a different height and they are not supposed to have same height. Difference in height does not qualify this as missing value of height. Plz correct me if I am wrong. Regards
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Hi, Could anyone please tell me which feature selection methods SAS enterprise miner supports and how can I use them? I have examples like information gain, gain ratio, chi-square, variable clustering, correlation matrix, association rule mining using apriori algorithm, ensemble of randomized decision trees for determining feature importance, Fisher Score and top feature selection Regards
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Hi, I was wondering if someone could help me? I am applying machine learning algorithms on my dataset using SAS enterprise miner, my dataset consists of three columns named file name, feature name and feature type. Each feature name has a distinct feature type. A file may have multiple feature names and obviously feature type as well. for example file name "A" has sometimes 3 or more rows having different features (But not more than 15). There are in total 70 unique feature names and 24 feature types. One person suggested me to input missing values by inserting remaining missing columns and types as missing. But my point is, for example file "A" had only 3 rows and has 67 missing, file "B" had 11 rows and 59 missing, in that case if I insert 67 or 59 more rows for each feature name and declare them as missing then I would have more missing values than my existing original values which may impact on my results when i apply classifiers on them. Could anyone tell me whether it is right or wrong to calculate these kinds of missing values? could you tell me why? A rough table shows what I am trying to figure out File name Feature name Feature type A F1 D1 A F2 D15 A F3 D7 B F1 D1 B F5 D18 B F35 D10 B F20 D13 B F45 D16 A F4 Missing A F5 Missing B F2 Missing Regards
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Hi, I was wondering if someone could answer couple of questions, I am performing malware analysis and have a dataset of around 1000 malwares and 1000 clean applications, my malwares belong to 7 different platforms like Intel, ARM (200 malware) , AMD etc and clean applications belong to only one class of ARM (1000 applications). Someone told me to either reduce malware to 200 to match the right platform in clean apps (means i may have 400 in total both malware and clean apps) or create a dataset of 200 malware but keep same 1000 clean apps which would create an imbalance dataset. I had something in mind to create another dataset of 200 arm malwares and add some malwares from each platform lets say 50 each along with same 1000 clean apps and create a second dataset to with remaining malwares to test whether my machine learning algorithms is generalized or not? is it able to detect other malaware or not?? Please suggest which of the approach, I should proceed in order to get a right track. is it going to create any impact on my classifiers? Please suggest any literature about it if possible. Regards
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Hi, I am using Cutoff node and trying to see Recall score but cannot find it. I have set the parameter of Cutoff method to Event Precision Equal Recall. In the results I get Overall precision rate and other various values but cannot figure out how would I get recall values and from that value how can I measure f-score/measure. Regards
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Hi, I wonder if someone can help me, I am using model comparison node and getting following kind of classification table for validation and training datasets. False True False True Negative Negative Positive Positive 364 4518 253 825 Can anyone please tell me how do we get these for testing dataset? In general do we need to find out for test datasets? When we write these readings do we write aggregated values or for individual values? Regards
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Hi Wendy, Thanks for your message, i am able to enter this code. Could you also tell me is this option going to work for all HP Forest nodes(In case I have multiple nodes) or I can use it for a separate node as well. I have a standard HP Forest node and I am looking to add a another node separately on which I am looking to implement this code as a conditional decision tree. So, what should I do in this case?
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Hi Padraic, I was able to apply Decision Stump but could not find PRESELECT option in any of the node properties to get conditional decision trees. Would you please help me about it? Regards
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