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Questions tagged [supervised-learning]

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.

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What is the difference between supervised learning and unsupervised learning?

In terms of artificial intelligence and machine learning, what is the difference between supervised and unsupervised learning? Can you provide a basic, easy explanation with an example?
TIMEX
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Scikit-learn: How to obtain True Positive, True Negative, False Positive and False Negative

My problem: I have a dataset which is a large JSON file. I read it and store it in the trainList variable. Next, I pre-process it - in order to be able to work with it. Once I have done that I start the classification: I use the kfold cross…
Euskalduna
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Can anyone give a real life example of supervised learning and unsupervised learning?

I recently studied about supervised learning and unsupervised learning. From theory, I know that supervised means getting the information from labeled datasets and unsupervised means clustering the data without any labels given. But, the problem is…
krupal
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Convolutional Neural Network (CNN) for Audio

I have been following the tutorials on DeepLearning.net to learn how to implement a convolutional neural network that extracts features from images. The tutorial are well explained, easy to understand and follow. I want to extend the same CNN to…
moeabdol
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Calculate sklearn.roc_auc_score for multi-class

I would like to calculate AUC, precision, accuracy for my classifier. I am doing supervised learning: Here is my working code. This code is working fine for binary class, but not for multi class. Please assume that you have a dataframe with binary…
Aviade
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Naive Bayes vs. SVM for classifying text data

I'm working on a problem that involves classifying a large database of texts. The texts are very short (think 3-8 words each) and there are 10-12 categories into which I wish to sort them. For the features, I'm simply using the tf–idf frequency of…
Ryan
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what is distant supervision?

According to my understanding, Distant Supervision is the process of specifying the concept which the individual words of a passage, usually a sentence, are trying to convey. For example, a database maintains the structured relationship concerns(…
smatthewenglish
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How to compute precision,recall and f1 score of an imbalanced dataset for K fold cross validation?

I have an imbalanced dataset containing a binary classification problem. I have built Random Forest Classifier and used k-fold cross-validation with 10 folds. kfold = model_selection.KFold(n_splits=10,…
Jayashree
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What is the difference between X_test, X_train, y_test, y_train in sklearn?

I'm learning sklearn and I didn't understand very good the difference and why use 4 outputs with the function train_test_split(). In the Documentation, I found some examples but it wasn't sufficient to end my doubts. Does the code use the X_train to…
Jancer Lima
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Can a model have both high bias and high variance? Overfitting and Underfitting?

As I understand it when creating a supervised learning model, our model may have high bias if we are making very simple assumptions (for example if our function is linear) which cause the algorithm to miss relationships between our features and…
Alaa Awad
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Time Series Ahead Prediction in Neural Network (N Point Ahead Prediction) Large Scale Iterative Training

(N=90) Point ahead Prediction using Neural Network: I am trying to predict 3 minutes ahead i.e. 180 points ahead. Because I compressed my time series data as taking the mean of every 2 points as one, I have to predict (N=90) step-ahead…
alper
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metric learning and contrastive learning difference

I researched some materials，and know that the goal of contrastive learning and metric learning are both to learn such an embedding space in which similar sample pairs stay close to each other while dissimilar ones are far apart. But what is the…
JustinGong
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Why Gaussian radial basis function maps the examples into an infinite-dimensional space?

I've just run through the Wikipedia page about SVMs, and this line caught my eyes: "If the kernel used is a Gaussian radial basis function, the corresponding feature space is a Hilbert space of infinite dimensions."…
PDani
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Use pretrained model with different input shape and class model

I am working on a classification problem using CNN where my input image size is 64X64 and I want to use pretrained model such as VGG16,COCO or any other. But the problem is input image size of pretrained model is 224X224. How do I sort this issue.…
Pankaj Kumar
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Supervised learning(document classification) using deep learning techniques

I was reading the papers on deep learning. Most of them refer to unsupervised learning. They also say the neurons are pre-trained using unsupervised RBM network. Later they are fine tuned using Back propagation algorithm (supervised). So can we…
alex
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