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Questions tagged [machine-learning]

Machine learning algorithms build a model of the training data. The term "machine learning" is vaguely defined; it includes what is also called statistical learning, reinforcement learning, unsupervised learning, etc. ALWAYS ADD A MORE SPECIFIC TAG.

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28 views

Best way to split real valued imbalanced distribution into finite number of classes

What I'm trying to do is adapting this research paper to another problem. In short: the authors split price variations of S&P500 index into four different classes. Then they train a Random Forest ...
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11 views

Optimal $\gamma$ parameter for the Exp3 algorithm

What is the optimal $\gamma \in (0,1]$ parameter of the Exp3 algorithm for the multi-armed bandit problem, given a fixed number of arms $K$? In my experiments, there seems to be an optimal value but I ...
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1answer
42 views

What is the equation to calculate the sample size for A/B testing? [closed]

I have been searching for information on how to calculate sample size for A/B testing. My research was fruitless. I found many online calculators, but not any blog/paper that explains how the ...
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1answer
18 views

Combining random forests and neural networks

Let's suppose the following toy example: we are given the task of estimating how many years a person has yet to leave. For this problem we have tabular data such as age, height, ethnicity, etc; and ...
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26 views

Advantage of inputs/targets to be normally distributed?

Why is it advantageous for inputs/targets to most ML algorithms like neural nets to be normally distributed? I am not talking about mean normalization, but in some cases of skewed data, people perform ...
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1answer
43 views

General formula for AR($p$) auto-regressive time series

I'm trying to find a reference (including the full formula) for the following. If $X_n = a_1 X_{n-1} + \cdots a_p X_{n-p} + e(n)$ where $\{e(n)\}$ is a white noise, then $$ X_n=g(e_0,e_1,\ldots,e_n)+\...
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11 views

How is jaccard similarity used to find the similarity between Bootstrap samples when measuring stability of EM?

Im reading the answer on "how to determine number of clusters in EM algorithm". How to tell if data is "clustered" enough for clustering algorithms to produce meaningful results? One of the ...
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19 views

What model to use to predict multiple binary time series

I have dataset similar to above one. It contains ~ 1 lakh observations and and ~ 30 columns (those are my columns). I want to predict whether a particular individual is likely to watch television in ...
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23 views

ISLR - linear regression, error of coefficients and resample method

ISLR is the famous book "Introduction to Statistical Learning - with applications in R", used for many people that are studying data science as me. Reading the 3rd chapter of ISLR, I have a question ...
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5 views

Should we take antilog of the target value before fitting data into model?

I' am a noob in Data Science and was working on House Price Prediction data set on Kaggle in which the target value i.e 'SalePrice' is left skewed. To make the distribution normal, I used log ...
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14 views

does multicolinearity affect classification models? any type of classification models like SVM, ANN, Random Forest,etc

I have been working on a dataset with more than 50 variables and I know some of the variables are highly correlated(>0.8). so for my multi-class classification problem should I worry about ...
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8 views

Does Follow-The-Leader incur log(T) regret on quadratic loss functions?

I encountered this statement in multiple lecture slides I found through duckduckgo, but I found no proof, and it doesn't seem trivial for the general case. Can anyone verify the $log(T)$ regret bound ...
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2answers
84 views

Time Series Forecast with Neural Networks blows up

I programmed a feedforward neural network for forecasting a time series, but the forecast is not stable and reasonable. I used a non-seasonal lag of 3, hence produced a gliding window of 3 as input ...
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6 views

Matching for disease prediction

I'm new to using machine learning in healthcare and I am learning about matching in studies. If one wants to predict the probability of a person getting a particular disease based on the previous ...
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0answers
43 views

Polynomial Regression model fits poorly

I was reading ISLR and implemented the least squares approach for a linear regression model on the autos data set which comes with the book. For the least squares approach I used only one predictor(...
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0answers
19 views

GAM goodness-of-fit under covariates shift

I have GAM models that were fitted using PyGAM and its defaults for fitting a linear GAM. To validate the model I do a leave-one-out cross-validation looking at either a Spearman's rank correlation ...
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1answer
32 views

How does the penalized form of RSS (residual sum of squares) work?

In another word, how to reverse engineering the equation (5.9) by explain all the assumption and reasoning after the plus sign of (5.9) in Elements of Statistical Learning. Note: I had used the ...
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14 views

How to determine the proper number of samples for development/test set in machine learning

I would like to know if there is a statistical/empirical method to determine the proper number of samples for development/test set used for testing the generalized ability of a machine learning model. ...
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0answers
7 views

Stopping Criteria for Pre-Training model

In stacked autoencoders during greed layer-wise training of individual autoencoders using gradient descent and backpropagation to minimize the reconstruction error mean squared error. What is the ...
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1answer
27 views

How valid is this Stacking Model (input features to weak learners are different)?

I have a set of features with 6 of them being categorical, 1 continuous and 2 textual in type. I have to predict the labels ( 10 in number) for them. I tried applying several models and came to a ...
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0answers
14 views

Find parameter vector based on objective function

I would like to find the set of parameters $\alpha$ (i.e. $\alpha$ is a vector of parameters of dimension [33x1]) that satisfy the following conditions: $min_{\alpha}{\sum_{t=1}^{16} (\Delta D_{t} - \...
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44 views

How exactly keras LSTM layer works?

I try to create a sentiment analysis that have 7 classification. Let's say, I have 100.000 unique word (already converted into 100.000 integer) which have the longest input is 41. I created 3 layer ...
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18 views

how to select training and testing data for interpolation in 100 instances data?

I would like to divide my data of only 100 instances into training and testing an use the training data to fit a curve(interpolate) and use the testing data to calculate the error at the interpolated ...
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1answer
38 views

How to deal with categorical dependent variable with more than 2 categories?

I've been struggling to understand how to approach this problem. Problem Description I have $n$ features that describe a dog race such as: Final time First bend time Track Grade My dependent ...
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1answer
54 views

How does SGD come in the picture for Sequence to Sequence models?

I was learning that seq2seq models (from the deeplearning.ai course) try to maximize: $$ \max_{y} P_{\theta}(y_1 \dots y_{T'} \mid x_1 \dots x_T ) $$ I learned that one way they do it is via beam ...
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16 views

Use Lift charts with multinomial classification model

This is the example I’m trying to understand. I’m trying to understand the use of Lift charts with multinomial classification model in the evaluation phase. I can see only one category can be ...
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0answers
22 views

Which dataset should I rely on to determine when a model is converged?

I'm confused about the plot of model accuracy during the training. As you can see, the model seems converged in less than 10 epochs based on the validation set. However, in terms of the training set, ...
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1answer
13 views

Is Crossover only connected to Genetic algorithm?

Is the idea of crossover only restricted to Genetic Algorithms ? Are there any other evolutionary algorithms that uses crossover(even under another name ) ? If an algorithm uses crossover but does ...
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2answers
31 views

Machine learning models for regression on small data sets

What are the "best" models to be used for simple regression of 1 numerical variable using only a small data set of e.g. 250 samples and up to 10 features? I understand that the data set is super ...
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1answer
29 views

How to interpret Random Forest variable importance vs. distribution of min depth plots?

I am using Random Forest (regression) to analyze data on civil conflict. I have plotted two different things: variable importance and the distribution of the min depth (using the package randomForest ...
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1answer
20 views

vector support machine - margin?

I'm trying to understand SVM (Support Vector Machines) and there is a single technicality I don't understand. In Wikipedia (and several other literature), the margins are described by the equations $...
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0answers
12 views

Cross validation in recommender systems

I am trying to figure this out. I get the idea of how the cross validation works in recommender systems. My question is regarding to the phrase observed ratings, what does it mean? I think it means ...
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0answers
18 views

How is the correlation matrix in table 3.5 in the book ISLR calculated? What does jt signify?

I was reading the book Introduction to statistical learning by Hastie and Tibshirani and there was this relationship between newspaper, radio & tv ads and their effect on sales in multiple linear ...
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9 views

Can annotation confidence affect MCAR missing feature values?

Concerning binary image classification, I've a dataset with over 4500 dimensional CNN and GIST features, though many of the feature values are missing NaN. This is ...
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1answer
56 views

How do I read an auto-correlation plot?

I'm taking a data camp lesson by Professor Rob J Hyndman. He went over the ACF plot and said that you know the period of seasonality based on the highest point in ...
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1answer
34 views

Difference between agglomerative and divisive clustering in terms of results?

Both methods in Hierarchical clustering have always the same result (number of clusters and instances in the same clusters) and the difference is only the way they use to compute the result? Or the ...
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1answer
27 views

Sampling Methods : Pattern Recognition and Machine Learning Bishop

I am reading chapter 11 . Sampling Methods from the book : Pattern Recognition and Machine Learning by Bishop : In the introduction , in short,he evaluates expectation of some function $f(z)$ with ...
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0answers
26 views

Model Not Performing Well On Validation Data - Customer Attrition Modeling

I am modeling customer churn for the online subscription. I looked back 90 days into customers’ data, using number customer watching behavior etc. I get a pretty strong model based on test data. <...
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0answers
21 views

Understanding K-fold Cross Validation [duplicate]

I have a doubt regarding the cross validation approach and train-validation-test approach. I was told that I can split a dataset into 3 parts: Train: we train the model. Validation: we validate and ...
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4answers
170 views

Cross Validation Vs Train Validation Test

I have a doubt regarding the cross validation approach and train-validation-test approach. I was told that I can split a dataset into 3 parts: Train: we train the model. Validation: we validate and ...
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0answers
13 views

Predictive Distribution in Gaussian Process Derivation

In Gaussian Process for Machine Learning (Rasmussen and Williams), on p.11, we are given the following predictive distribution: $$p\left(f_{*} | \mathbf{x}_{*}, X, \mathbf{y}\right)=\int p\left(f_{*} ...
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0answers
12 views

Empirical Comparison: which ideal data characteristics are best captured by each type of machine learning model?

I have reached the point as a data scientist where the empirical differences between the different types of regression models (leaving out classification only for simplicity) have started to matter ...
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1answer
41 views

Why is random sampling a non-differentiable operation?

This answer states that we cannot back-propagate through a random node. So, in the case of VAEs, you have the reparametrisation trick, which shifts the source of randomness to another variable ...
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1answer
149 views

Gaussian process - Why adding data points cannot increase the predictive bias?

I've seen this question here: How to increase variance in Gaussian Process regression? And trying to complete the proof. I'm looking at this book: Rasmussen & Williams 2006: Gaussian Processes ...
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1answer
75 views

Classifier which minimizes inaccuracy

I recently interviewed for a machine learning job which involved very mathematically rigorous questions. This is one of them, which I'm still very confused about. Question: Given a data generating ...
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0answers
15 views

what is the difference between a multilayered autoencoder and a hierarchical latent variable model?

I have been trying to understand how hierarchical latent variable models are different from multilayered autoencoders and in specific the argument below Autoencoder networks resemble in many ways ...
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0answers
21 views

Statistically evaluating classification accuracy of machine learning model

Let's say I'm trying to evaluate a classification algorithm and suppose there are $m$ data points in my test set. Here's my understanding so far: assuming my evaluation metric is the classification ...
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0answers
23 views

How to compare Accuracy of two RandomForest models? (Chi Sqr or Cohen's H?)

I've got two dataset which have exactly the same structure (15 features, 1 class variable with 7 categories) and roughly the same amount of observations). I trained a Random Forest with the full ...
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0answers
38 views

Is it possible that PCA works better without data scaling? [duplicate]

I am a beginner. I have a dataset of 1700 samples with 4 features and I have to perform Hierarchical Clustering (the agglomerative version) and I need to decide whether or not to scale the data and ...
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0answers
17 views

SVM in the classification layer of a Feedforward neural network

I want to use SVM in the classification layer of a 2 layer feedforward neural network. Need guidance from the community on how to approach this problem. This involves capturing the features from the ...