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]
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What are supervised ML Classification algorithms?
What I found are:
1. Naive Bayes classifier
2. K nearest neighbors classifier
3. Decision tree Algorithms(C4.5, Random Forest)
4. Kernel Discriminant Analysis
5. Support vector machines
If any other, can someone please help me with the remaining…
cloudybunny
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Store Decision Tree Code / Algorithm
I have an assignment for which I am planning on using machine learning (supervised learning in particular, such as a decision tree). The final code will be run on a Teaching Assistant's pc which doesn't have scikit learn or other external libraries.…
jfalkson
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Weight Vectors in Perceptron Algorithm in Machine learning
I'm researching on the perceptron Algorithm in machine learning.Till now,I understood the following things about the perceptron
1)It's a supervised learning technique
2)It tries to create a hyper plane that linearly separates the class
labels…
shahid Afridi
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Is supervised learning synonymous to classification and unsupervised learning synonymous to clustering?
I am a beginner in machine learning and recently read about supervised and unsupervised machine learning. It looks like supervised learning is synonymous to classification and unsupervised learning is synonymous to clustering, is it so?
lakhujanivijay
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Predicting opening and closing of an organisation
Given Training data of an organisation meter reading recorded at an interval of 15 minutes each day .Like for some N days we will be provided with data.
And Now with help of this data we need to tell that on a particular day an organisation is…
somu Boy
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Machine Learning Text Classification technique
I am new to Machine Learning.I am working on a project where the machine learning concept need to be applied.
Problem Statement:
I have large number(say 3000)key words.These need to be classified into seven fixed categories.Each category is having…
user1648855
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Word sense disambiguation using WEKA
I've got a training DataSet and a Test DataSet. How can we experiment and get results ?
Can WEKA be used for the same ?
The topic is Word Sense Disambiguation using Support Vector Machine Supervised learning Approach
The Document types within both…
Krithi07
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Can I calculate TP,TN, FPR and FNR in multiclass
If i classify data in 5 class, I get confusion matrix in 5 class classification but I can not calculate it
4822 18 9 0 40
0 1106 0 0 0
0 2 1990 0 0
0 0 1 2000 0
0 0 0 0 12
Can I…
PTZ
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Trouble Setting Up Multiple Algorithm Trials in Scikit-Learn
I am trying to use this example from the sklearn documentation. I am not really sure what the code is doing and although I assume I am inputting my dataset the wrong way, I recently obtained this error:
in…
Avi Mosseri
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What should be the value of parameters of neural network having large data sample?
I have done coding for neural network in Python for the multi-layer,feed-forward, back-propagation structure. In this network structure I have 24 nodes in input layer, 18 nodes in hidden layer and 1 node in output layer. I am getting the good…
lkkkk
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Binary classification with KNN
I post here because I don't know how to improve the performance of my binary KNN.
The problem is that I have 99.8% Specificity and only 82% Sensitivity, but I'd rather have more Sensitivity than Specificity.
I'm new to this field, and I've only been…
user4134164
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Leave one out - MATLAB
I was trying to classify a dataset using the following strategy:
Leave one out cross validation
KNN to classify (count the number of errors) for each "fold"
Calculate final error
repeat for k=[1,2,3,4,5,7,10,12,15,20]
Here's the code, for the…
Pedro Álvaro Chagas
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Using match scores to determine right features (Machine Learning)
I am familiar with determining the extent of match of a given set of documents in our knowledge base against a search query document (based on cosine distance) once the features are available. We would map both on the vector space based on the…
raghu
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bic.glm model errors on prediction
I have created a model using bic.glm, and I am trying to predict probabilities on validation data which does not have the dependent variable 'is_blocked'.
When I run the predict() function on the validation data, I get the following error:
Error in…
Walter Williams
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Text anonymization using supervised machine learning
I have a lot of text documents containing company and personal names. I have aligned text documents where the above have been manually anonymized (names replaced with a single unique character).
I want to use this corpora to train a system to…
SupsH
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