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.

429 questions
25 views

Trying to Understand the Statistical Notation [on hold]

Does x need to be randomly sampled from a normal population and what is d?
28 views

Compering Facebook Prophet to supervised learning [on hold]

I'm working on a time series forecasting on one of my projects. I would like to compare the MAE (or any other scoring) of two different models, one is facebook prophet and the other one is ...
16 views

Forecasting time series data using EEMD based SVM?

Splitting of Dataset: Dataset = Train1 + Test1 EEMD(Train1) = train1 + test1 I am forecasting on time series data("Dataset") using SVM. First I found the Intrinic Mode Function(IMF) of time series ...
182 views

How would someone use curves as an input to a supervised learning model?

I was asked this question during a test and couldn't figure out the answer: You have a set of curves against time $X_i(t)$ that you want to use as input to a supervised learning model. The curves ...
14 views

How to interpret a high sensitivity and low specificity using svm classifier?

I want to have your opinion on the performance of my SVM classifier (k-fold cross validated): Classification of class1(n=45)/class2(n=86) Accuracy: 65.4% Sensitivity: 88.2% Specificity: 22.2% AUC:...
43 views

Recognizing whether a written and spoken number is the same

For our ML assignment we have three datasets. The challenge is about checking whether a written and spoken number refer to the same number. We're using the MNIST dataset with handwritten numbers, and ...
6 views

Can normalized values and original values be combined in a feature vector classification?

I standardized feature vector before SVM classification in MATLAB. The feature vector consists of time domain signal features and its normalizations i.e., feature vector is a combination of actual ...
11 views

When is the sum of model predictions the prediction of the sum?

The response variable in a regression problem, $Y$, is modeled using a data matrix $X$. In notation, this means: $Y$ ~ $X$ However, $Y$ can be separated out into different components that can be ...
25 views

Looking for ways to transform time-series data recorded from object movement into equation describing the movement direction of the object

Looking for some time-series data transformation advice! I want to know what's the best way to transform data of 9-tuples time series data of IMU (Inertia Measurement Unit) sensor, recorded from a ...
9 views

Supervised learning vs Regularity based learning

I have some confusion about regularity based learning and supervised learning. Are they in essence, not the same thing? We have some labelled data, and our algorithms are structured based on learning ...
9 views

What is the best method using supervised learning to label images with single-pixel-level defining features?

The images are in later form of size 170 where each index could be a -1, 0, or 1. The images are multi-labeled, with 8 possible labels. The issue is that there are often only slight differences ...
18 views

Linking generative, discriminative models to supervised and unsupervised learning

Definitions that I am considering: A generative model learns p(x,y) whereas a discriminative model learns p(y|x=x). I would like to verify if my understanding is correct by sharing the following ...
5 views

Determining an appropriate cost function given the type of problem and a hypothesis function

I'm studying up on machine learning basics and the standard high-level approach in supervised ML is to define a hypothesis function that maps inputs to outputs. Then define a "cost function" that ...
35 views

Top principal components versus most significant random forest variables

I was working on making a supervised learning model starting with a database of about 100 features and 1000 data entries. My goal is to predict a certain target variable. I tried three different ...