I am trying to analyze the titanic dataset and build a predictive model. I have preprocessed the datasets. Now while I am trying to predict using the test set and I don't know why it doesn't show any result.
Titanic_test.arff
Titanic_train.afff
If you open the two files (training and test set) you will notice a difference: in the training set the last column has value 0 or 1, whereas in the test set it has ? (undefined).
This means that your test set doesn't contain the answers, therefore Weka cannot do any evaluation. It could do predictions though.
I'm currently trying to make predictions on re-trained Inception-V3 model in TensorFlow.
When I'm trying to run inference on image with
bazel-bin/tensorflow/examples/label_image/label_image \
--graph=/path/output_graph.pb --labels=/path/output_labels.txt \
--output_layer=final_result \
--image=/path/to/test/image
I'm getting an error
E tensorflow/examples/label_image/main.cc:303] Running model failed: Not found: FeedInputs: unable to find feed output Mul
I used transfer learning to fine tune Inception trained on Imagenet dataset, to train on my own 1000+ classes. Training & evaluation processes were ok. I exported graph with tf.train.write_graph() and freeze it with https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py
Did anyone faced this problem??
It seems that in the graph you are using, the feed or "input_layer" node has been renamed and is no longer called "Mul". You need to find the name of the node where inputs should be injected into your saved graph, and pass the node name via the --input_layer flag.
The easiest way to find the node name is just to make sure to set it explicitly to something you know when you build the graph in the first place.
I was trying to data model a Classification Machine Learning algorithm on a data set which has 32 attributes,the last column being Target class.I refined the attributes number in to 6 from 32 ,which I felt would be more useful for my Classification model.
I tried to perform J48 and some incremental classification algorithm.
I expected output structure which consists of confusion matrix,correctlt and incorrectly classified instances,kappa value.
But my result did not give any information on Correctly and Incorrectly classified instances.Also,it did not predict confusion matrix and Kappa value.All I received is like this:
=== Summary ===
Correlation coefficient 0.9482
Mean absolute error 0.2106
Root mean squared error 0.5673
Relative absolute error 13.4077 %
Root relative squared error 31.9157 %
Total Number of Instances 1461
Can anyone tell me why I did not get Confusion matrix,kappa and Correct,Incorrect instances information.
Unfortunately you didnt write your code, or what version of weka do you apply.
BTW, to calculate confusion mtx, kappa etc. you can use methods of Evaluation class, http://weka.sourceforge.net/doc.dev/weka/classifiers/Evaluation.html
for example, after you train your model:
classifier.buildClassifier(train); \\train is an instances
Evaluation eval = new Evaluation(train);
//evaulate your model at 10 fold cross validation manner
eval.crossValidateModel(classifier, train, 10, new Random(1));
System.out.println(classifier);
//print different stats with
System.out.println(eval.toSummaryString());
System.out.println(eval.toMatrixString());
System.out.println(eval.toClassDetailsString());
I use weka for text classification, I have a train set and untagged test set, the goal is to classify test set.
In WEKA 3.6.6 everything goes well, I can select Supplied test set and train the model and get result.
On the same files, WEKA 3.7.10 says that
Train and test set are not compatible. Would you like to automatically wrap the classifier in "inputMappedClassifier" before porceeding?
And when I press No it outputs the following error message
Problem evaluating classfier: Train and test are not compatible Class index differ
: 2!= 0
I understand that the key is Class index differ: 2!= 0.
However what does it mean? Why it works in WEKA 3.6.6 and not compatible in WEKA 3.7.10?
How can I make the test set compatible to train set?
When you import the supplied test set, are you selecting the same class attribute as the one that you use in the train set? If you don't change this field, weka selects the last attribute as being the class automatically.
I am trying to figure out WEKA and perform some experiments with data that I have.
Basically what I want to do is take Data Set 1, use it as a training set. Run a J48 Decision Tree on it. Then take Data Set 2 and run the trained tree on it, with the output of the original data set with a extra column for what the prediction was.
Then do the same thing again with the Bayes Neural Network.
Can someone point me to a link of detail instructions on how exactly I would accomplish this? I seem to be missing some steps and cannot get the output of the original data set with the extra column.
Here is one way to do it with the command-line. This information is found in Chapter 1 ("A command-line primer") of the Weka manual that comes with the software.
java weka.classifiers.trees.J48 -t training_data.arff -T test_data.arff -p 1-N
where:
-t <training_data.arff> specifies the training data in ARFF format
-T <test_data.arff> specifies the test data in ARFF format
-p 1-N specifies that you want to output the feature vector and the prediction,
where N is the number of features in your feature vector.
For example, here I am using soybean.arff for both training and testing. There are 35 features in the feature vector:
java weka.classifiers.trees.J48 -t soybean.arff -T soybean.arff -p 1-35
The first few lines of the output look like:
=== Predictions on test data ===
inst# actual predicted error prediction (date,plant-stand,precip,temp,hail,crop-hist,area-damaged,severity,seed-tmt,germination,plant-growth,leaves,leafspots-halo,leafspots-marg,leafspot-size,leaf-shread,leaf-malf,leaf-mild,stem,lodging,stem-cankers,canker-lesion,fruiting-bodies,external-decay,mycelium,int-discolor,sclerotia,fruit-pods,fruit-spots,seed,mold-growth,seed-discolor,seed-size,shriveling,roots)
1 1:diaporth 1:diaporth 0.952 (october,normal,gt-norm,norm,yes,same-lst-yr,low-areas,pot-severe,none,90-100,abnorm,abnorm,absent,dna,dna,absent,absent,absent,abnorm,no,above-sec-nde,brown,present,firm-and-dry,absent,none,absent,norm,dna,norm,absent,absent,norm,absent,norm)
2 1:diaporth 1:diaporth 0.952 (august,normal,gt-norm,norm,yes,same-lst-two-yrs,scattered,severe,fungicide,80-89,abnorm,abnorm,absent,dna,dna,absent,absent,absent,abnorm,yes,above-sec-nde,brown,present,firm-and-dry,absent,none,absent,norm,dna,norm,absent,absent,norm,absent,norm)
The columns are: (1) data instance number; (2) ground truth label; (3) predicted label; (4) error; (5) prediction confidence; and (6) feature vector.