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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 ...

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Linear Discriminant Analysis as Dimensionality Reduction very sensitive to Training Set size

I'm working with supervised classification of object-based satellite imageryand currently investigate different dimensionality reduction methods on their suitability to this application. As part of my ...
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47 views

How to define a time series classification problem?

I have 3 sets of time series data generated from sensors, I believe they have some correlation themselves. Certain "modes" of the system can be defined from the patterns from these signals. The signal ...
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1answer
45 views

Knn Decision boundary

I am new to machine learning and trying to draw decision boundary for k nearest neighbor where k=3. I know that the decision boundary for k=1 would be the perpendicular bisector between two different ...
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8 views

What causes a high testing deviance vs. training deviance in a gradient boosting classifier?

My main goal is to classify multi-class data using supervised learning. Currently, I am looking into GradientBoostingClassifier as the estimator. I want to make sure I am selecting the model ...
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10 views

What is supervised learning using execution traces?

In the context of machine learning, in particular, RNNs, what's supervised learning using execution traces? What are "execution traces"? When can we use this type of learning? What are the benefits (...
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28 views

Classification model on a highly unbalanced dataset [duplicate]

I’m dealing with a highly unbalanced dataset where 20% of data belongs to class A and 80% belongs to class B. It’s very hard for us to produce synthetic class A data. Just wondering if the below ...
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1answer
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Is supervised learning a subset of reinforcement learning?

It seems like the definition of supervised learning is a subset of reinforcement learning, with a particular type of reward function that is based on labelled data (as opposed to other information in ...
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1answer
34 views

Regression with multidimensional output variable Y

Say we have an $N \times q$ matrix $Y$ with $N>q$. Also, we have an $N \times p$ data matrix $X$. We are interested in a model of $Y = X \times W + \epsilon$, where $W$ is a $p \times q$ matrix ...
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1answer
251 views

Training error in KNN classifier when K=1

I got this question in a quiz, it asked what will be the training error for a KNN classifier when K=1. What does training mean for a KNN classifier? My understanding about the KNN classifier was that ...
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31 views

what's the difference between semi-supervised learning and partially supervised learning? [closed]

Isn't every semi-supervised problem also a partially supervised learning problem and vice versa?
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25 views

How can I tell a model reached the optimal parameters?

Aside from stacking more models, If I want to know if I have arrived the best possible single model(the best parameter), is there anything/process I can tell? Assume I made n-degree of polynomial ...
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31 views

What exactly is semisupervised learning?

I have come across two descriptions of what semisupervised learning is, where one would have a small set $\mathcal{L}$ of labeled data and a larger set $\mathcal{U}$ of unlabeled data. The first ...
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17 views

confusion about multiclass linear classifier

I notice that there is a bit of confusion in multiclass linear classifier notation in at least 2 points: from Bishop's book and for example these slides they call the One-versus-the-rest approach (...
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24 views

Using ML supervised techniques for optimal marketing allocation

First, please, be patient. I'm just new to this community :) Let's say we have a target variable (ie: Marketing ROI) we want to maximize and suppose we can gather (clean) historical data (ie: monthly/...
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19 views

LSTM frame time series to a supervised learning problem

I just begun to play around with LSTM. Therefore I read the guide from this site Multivariate Time Series Forecasting with LSTMs in Keras The task is to predict the air pollution. I understand the ...
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1answer
12 views

Improving supervised learning for question text comprehension when there is no obvious answers

I'm trying to determine how to answer question from text with supervised learning. This used to work quite well when every questions had answers. Here is the head of dataset we used with the sentence ...
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1answer
16 views

clustering VS supervised classification, in the case of very small database

I'm trying to classify/cluster subjects according to 4 features in two classes: healthy and sick. Two things to know: I know the labels/classes of each subject + I only have 40 subjects (in total: ...
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1answer
29 views

unsupervised classification VS supervised classification when data labels are known

Can someone give me some scenario where it's better to use clustering (unsupervised classification) than supervised classification such as SVM ? I mean in a case where you know the data labels/classes....
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7 views

Semisupervised and Multiclass Classification

I have a dataset that includes around 400 instances (400 users' instances) with 10 features. As follows: ...
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28 views

(Re)-Train on a small dataset and new incoming data

I would like to train a classifier (doesn't matter which learning algorithm) on a small set of training data. As soon as the system predicts new samples, it should collect them, add the samples to the ...
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3answers
399 views

What *is* an Artificial Neural Network?

As we delve into Neural Networks literature, we get to identify other methods with neuromorphic topologies ("Neural-Network"-like architectures). And I'm not talking about the Universal Approximation ...
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1answer
166 views

Model for predicting chance of winning in variable count of opponents

I have dataset with horse racing results including bookie odds - converted to percentage chance of winning. Data are stored in relation tables. The basic entity relation is described on image. Each ...
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44 views

What are the disadvantages of Random Forest Algorithm? [duplicate]

I am using random forecast algorithm via python sklearn library to forecast data. So far it's accuracy on my training data is good. I am using the algorithm to find the predict a decision based on ...
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27 views

Method of finding optimal parameters - Supervised learning versus reinforcement learning

How to choose the best approach to optimization problem? I have a working simulation of the problem at hand that is slow and want a quick way to determine the optimal operating parameters based on a ...
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31 views

How to add predictors to LSTM time series model? [duplicate]

I'm doing Long-Short-Term-Memory (LSTM) to forecast time series. I was wondering, could we add x-reg part to an LSTM model? Like adding an X-Reg part to and ARIMA model? Say I have a response monthly ...
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1answer
87 views

What is the difference between model selection and hyperparameter tuning?

In the context of supervised learning, in most statistics based texts and papers, one reads about model selection. For example Hastie, Tibshirani and Friedman in ESL define it as: Model Selection: ...
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20 views

Forecasting with two rank correlated data sets

I have a machine that produces widgets. I know the more widgets the machine produces the more heat it produces. When I observe the machine I see the following temperatures: [20,21,19,25,30,40,45,47,...
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1answer
133 views

SVM: Why alpha for non support vector is zero?

In the optimization problem in SVM to compute the margin, we use Lagrange multipliers to insert the constraint: $L(w,b,\alpha)= \frac{1}{2}|w| - \sum \alpha (y_i(w*x_i+b) -1) $ Now we want to compute ...
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40 views

Cross Validation - Are folds and reps learner dependent?

If I read relevant papers I often get the advice to use 10 fold CV or repeated CV instead of a 5 fold or 3 fold CV for tuning a certain learner. The reason is that especially the 10 fold CV with ...
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3answers
147 views

What do we mean when we say that an approach is “Bayesian”? [duplicate]

I'm trying to explain to a nontechnical colleague of mine what a Bayesian approach is. I realized that despite having used Bayesian methods on more than one occasion in the past, I don't have an ...
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1answer
35 views

Is there any work done on reconfigurable convolutional neural networks?

Convolutional Neural networks are used in supervised learning meaning models are always "set in stone" after training (architecture and paramters) so this might not even be possible, but is there any ...
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8 views

Classify a specific object amongst other diverse objects

I have a device which takes one picture per day of a slab. It contains many instances of a specific object (let's call it "Object A") and a few other objects (let's call them "Others"). I want to ...
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78 views

Selection of random forest regression models based on r2_score

I'm making a regression model which predicts the concentration of air pollutant. It consists of the following features: Features Things that I have done so far : Assigned mean values to the missing ...
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1answer
14 views

How to take advantage of the information/structure we have in the labels in multiple output regression?

I have a regression problem where each observation possesses a vector of features and 4 associated responses. These responses, as in many problems are correlated. It would be nice to be able to ...
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1answer
21 views

n * one_hot VS n_hot encoding for modeling input layer for a card game

How should I design my input layer for the following classification problem? Input: 5 cards in a card game; vocabulary is 52 cards Output: some classification using a neural network How should I ...
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33 views

Scikit Learn Learn Regression Problems & Scale

I have questions on the problem forms from Scikit learn regression. I notice that for Lasso regression, Scikit learn try to min 1/2n. What is the purpose for that? And where does it come from? Why it ...
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1answer
43 views

Linear Regression Models: Behaviour

If the target value in the training data is in the range $[5,11]$. Is it possible for a linear regression model to predict absurd values between $[-10, 300]$ (not the exact range but close) in the ...
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Learning with noisy labels - how much more data do we need?

Suppose we have want to perform supervised learning on a dataset with binary labels. The training set is of size N, and we achieve performance (let's say, accuracy) of A% on the test set. Now, ...
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1answer
35 views

Classification followed by regression

I have the following problem: I have a dataset for which my observations have a bunch of features and a continuous response (regression problem). However, some of my observations (about a fourth of ...
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How to deal with partial/unshared features in a dataset?

Sorry for the somewhat ambiguous title but I was not sure how to describe the problem in one line. The issue I am having is the following: In a supervised learning setting, I have instances that have ...
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137 views

Training, validation, testing. How to tune the parameters and hyperparameters for production

I understand while building machine learning models, one uses training and validation datasets to tune the parameters and hyperparameters of the model and the testing dataset to gauge the performance ...
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1answer
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Correct feature aggregation for this tricky buying problem

Consider the following problem, which was asked in an interview I was at (but it wasn't directed at me). It seems deceptively simple, but then it turns out to actually be really hard to answer well: ...
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33 views

Tree complexity in boosted regression trees [duplicate]

In boosting regress trees, there are many tuning parameters and I am interested in the tree complexity also called the interaction depth while using the R package GBM: I want to understand more ...
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1answer
21 views

Rule based ISLE Ensemble Generation

I come through a algorithm ISLE Ensemble Generation in machine learning. The following is the steps given in Elements of Statistical Learning: But I am unable to apprehend it and implement it in ...
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207 views

How to identify if a problem is a good candidate for applying machine learning?

Specifically, I am talking about supervised learning. It seems that a problem is a good candidate for applying ML if: We have fairly high-accuracy ground-truth labels in our dataset. The dataset is ...
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1answer
85 views

machine-learning : Why training set and test set need to be independent and identically distributed?

My machine-learning book that I'm reading only says that they need to be but not why? My intuition says that if they are that leads to a better learning, if they were not it would be like we are ...
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30 views

How can I compare feature importance values (coef) between two datasets?

Goal: I would like to find a way to compare the feature importance score (e.g.,"coef" values for SVM in scikit learn) for a feature (say "weight") for dataset A with the feature importance score for ...
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1answer
96 views

SVM classification on distance matrix

How can I do an SVM classification when I only have a distance matrix (pairwise matrix)? Edited: I want to classify my data in two groups: healthy and sick. My original data are histograms (which are ...
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13 views

Multiblock Linear Discriminant Analysis?

I have different type of data (quantitative and qualitative) and I am looking for a way to do a Multiblock Linear Discriminant Analysis. The same as Multiple factor Analysis but supervised since my ...