Supervised learning is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.
Questions tagged [supervised-learning]
542 questions
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One class classification with libsvm
A quick recap for what I want to do, I want to determine if a text is written by the same author or not. Thus I use one-class classification.
In my training set (18 samples), it looks like this (for simplifying, I used x as data value):
1 1:x 2:x…
Xitrum
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Automatically learning clusters
HI complete newbie question here: I have a table consisting of two columns. First column belongs to "bins" that are coded by where a the fruit flies live. The second column is either 0 or 1, neutral vs really like sugar, respectively. I have two…
Ahdee
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How to calculate FAR FRR Accuray using confusion matrix with more than 3 classes?
I have a 20x20 confusion matrix. How can I calculate FAR, FRR, Accuracy and Precision values which represent complete system? Not for each class, but for the whole system.
ruchzlive
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How can we use a machine learning algorithm on this type of data?
Here is the scenario:
We have a website with the capability for students to create an e-portfolio, which is like a profile page combined with your projects you can add to it.
For each student portfolio we are going to have an educator review the…
Nearpoint
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Representing a class by negative number for training in GBM in R
I have already generated a huge train file of ~1GB in which the ground truth is one of 4 classes {-1,0,1,2}. Yes, I agree that I could have done it using 3 instead of -1, but for some conventional reasons I used -1.
I want to use gbm in multinomial…
Bit Manipulator
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Can train a neural network with a training set with a label and test it with a dataset without a label?
I have a question that has been very persistent for me.
Can I train a neural network with a labelled dataset (i.e.: a dataset with the information of the target) and then apply another dataset without label?
I want to train the network with the…
Pedro Neves
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Real-time supervised learning with Weka (on Android phone)
I want to use accelerometer data to (try) make predictions what activities the user is doing (simple activities). Let's say I have a bunch of training instances, where is a single training instance and xn is the class label. After training, I want…
user678392
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WEKA : How to defferentiate numerical data that are 'missing' and 'not applicable'?
I am new with WEKA.
In my dataset, i have an attribute where the type is numeric. In the dataset, there are specific values being represented as 'missing value' and 'not applicable'.
For example
0- missing values
99999 - represents not…
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Regression trees with standard deviation reduction
I have a data set of 1k records and my job is to do a decision algorithm based on those records.
Here is what I can share:
The target is a continuous value.
Some of the predictors (or attributes) are continuous values,
some of them are discrete and…
SdSdsdsd
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Two output from neural network and one target
Is supervised training of a neural network with 2 unknown outputs possible where there is a relation such as y=a.x^b between known parameters (y,x) and unknowns (a,b). here (a,b) are the outputs of network!!!
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Machine learning example- determine if a website is business or personal
I have a machine learning problem. I am given a long list of domains and I have to figure out which are ecommerce websites and which are personal websites. It is kind of a difficult problem because I do not have any training data to work with. I…
user1893354
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How to approach a machine learning programming competition
Many machine learning competitions are held in Kaggle where a training set and a set of features and a test set is given whose output label is to be decided based by utilizing a training set.
It is pretty clear that here supervised learning…
Joy
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decision tree for predictive modelling
I have a satellite data that provides radiance which i use to compute Flux (using surface and cloud info). Now using a regression method, I can have a mathematical model relating radiance and flux and can be used to predict the flux for new…
Bijoy
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Learning curve for noisy data
I am doing a supervised classification of small texts, and the data is very noisy. I plotted a learning curve: x-axis is # instances. y-axis is the value of F-measure. The curve is falling: the more instances I use, the lower the F-measure score is.…
Alina
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Gradient Descent Algorithm
I recently implemented a gradient descent code for linear regression. But when I'm increasing the number of iterations, I'm instead getting increasing values of 'w' and 'c' proportional to the number of iterations. Can anyone please tell me where…
sanepunk
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