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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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Calculating the number of neurons and the number of hidden layers for a neural network MATHEMATICALLY

I have a fair idea that a lot of research has been done and is still underway to explore the science behind the black art of a neural network (NN) architecture, i.e., accurately calculating the number ...
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1answer
174 views

Random forest permutation test: Is permutation of the training set appropriate?

I have a rather high-dimensional data set (p > 1000) with several variables ranking significantly higher than the rest in terms of variable importance (measured by Gini impurity). However, these ...
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1answer
1k views

How to penalize a regression loss function to account for correctness on the sign of the prediction?

I am dealing with a regression problem (my targets could potentially take values between -inf to +inf). To optimise my model, I have two objectives: 1) Predictions should be close to the targets. 2)...
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5 views

Datasets for Document Classification problem [on hold]

I am doing a project to make a application that can take pdf and docx documents as input and classify them into various categories such as - Financial - Government and Political - Sports and ...
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7 views

KNN works in `class` but not `caret` (Too many ties) [on hold]

I am making a KNN algorithm to predict close_price with about 80,000 rows of this data. ...
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16 views

What does the intercept represent in a model matrix?

I am making a KNN algorithm to predict close_price with about 80,000 rows of this data. ...
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17 views

relation among loss function / MLE / Bayesian estimation

I have read a lot of stuff on the relation between minimizing a loss function / maximizing the likelihood / choose a centrality measure of the posterior (Bayesian estimation); but I cannot see a clear ...
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1answer
179 views

classification on imbalanced dataset via random forest: results vary with random seed

I have a highly imbalanced dataset of about 8000 observations, with 11 features and one binary target variable. I want to predict the target labels, considering that the "1" target label occurs for 1....
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1answer
19 views

What are general practises used to divide the data into training / dev and test set?

Example: I have am building a dog vs cat classifier and I have collected data from 15 countries. Europe: 1. UK 2. France 3. Germany 4. Italy 5. Finland Asia: 1. India 2. China 3. Japan 4. Russia 5. ...
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12 views

Haul's Correlation-based Feature Selection (CFS) formula spread

I want to use the Correlation-based Feature Selection (CFS) proposed by Haul. I found this formula where $r_{zc}$ is the correlation between the summed components and the outside variable, $k$ is ...
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19 views

Is Stochastic Gradient Descent sensitive to training permutation?

I've recently read that SGD (Stochastic Gradient Descent) is one of the most popular techniques for training Machine Learning algorithms, including DNNs (deep neural networks). However, my ...
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8 views

What is parameter chaining in CNN?

I have read an article about the benefits of CNN. One of the points was "parameter chaining". What does it mean? And how does it make CNN more convenient?
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1answer
19 views

Predict song genre using LSTM

I have a dataset of songs based on genres. For example, a song may hold {5, 2, 3} as scores set for Sentimental, Rock and Jazz. In total there are 800 songs sequentially arranged. I want to predict ...
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11 views

Is the prediction with and without mean normalisation different in Collaborative Filtering?

In case of Collaborative Filtering: Given an output matrix I wish to learn parameters $\Theta$ (Parameter Vector) and X (Feature Vector). Now if I mean normalise the output matrix the values of $\...
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1answer
17 views

How can I visualize and correctly interpret the multi-output of a random forest regression?

I have multiple inputs and multiple outputs for a model I am trying to build using SkiLearn's Random Forest Regression in Python. I have imputed the missing data, divided the columns where the Ys ...
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1answer
2k views

Is the Laplace/Lidstone smoothing parameter (talking about Multinomial/Bernoulli Naive Bayes) related to the particular structure of the dataset?

I'm working with Multinomial and Bernoulli Naive Bayes implementation of scikit-learn (python) for text classification. I'm using the 20_newsgroups dataset. From the scikit documentation we have: <...
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1answer
398 views

Feature selection step before decision tree?

I want to use rpart (a R package) to build a decision tree model. The data is a high-dimensional expression matrix, with ~50,000 predictors and ~500 samples. The response is a categorical variable. ...
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1answer
62 views

How to determine number of profile in a dataset derivated from repeated measures?

I'm currently working on datasets which have been derivated from repeated measures over time (blood concentrations). Actually the descriptors of these datasets are descripting the shape of the curves (...
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1answer
56 views

ANN produces different result every time it's run

I tried an artificial neural network (ANN) model. Using same data set, it gives a different answer every time I run it in MATLAB. Does anyone why this happens and can suggested the best way to analyze ...
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2answers
339 views

How does one know if normalizing is improves reconstructions in the task of auto-encoding?

I wanted to understand the performance on an algorithm in the auto-encoding task and compare understand if normalizing the data was a good idea or not and compare the performance when the data is ...
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1answer
364 views

Expected value of error in neural network

I wanted to take a look at the properties of the error vector that is propagating during backpropagation. The error vector $\boldsymbol{\delta}$ at layer $i$ is nothing more than the derivative of ...
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0answers
20 views

Is it valid to make a fraction out of output of naive bayesian classifiers?

I'm working on a model of a twitter network that attempts to determine the likelihood of a tweet being retweeted. For every user in the network, I have a list of all of the tweets by all of the users ...
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2answers
180 views

To provide dimensionality reduction, 1x1 convolutions are used, before passsing them through a 3x3, or 5x5 convolution in an Inception module.

To my understading what a 1x1 convolution does is gives an embedding of the (i,j)th entry of the feature map along its depth. Besides here some dimensionality reduction is also done. How will the ...
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22 views

What to choose?ML project or an Internship? [on hold]

This is slightly off topic but pretty serious for me. I am an undergraduate student in CSE, 3rd year. I am confused in whether to do internship or to make my own project in Machine Learning in my ...
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1answer
385 views

Why does Batch Normalization need moving averages besides to track model accuracy?

I was reading the new layer normalization (LN) paper and it mentioned that batch normalization (BN) batch normalization required moving averages. I was re-reading the paper but and it says the moving ...
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1answer
17 views

Practical interpretation of Precision-Recall AUC

I have a classifier with an AUC (PR) of 0.06 which I will use for a practical interpretation. My test set consists of three months of data with a total of 2,200,000 observations of which 0.03 are ...
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13 views

Manifold in the context of machine learning

In the Deep Learning book by Goodfellow et al. on page 158 it is stated: In the context of machine learning, we allow the dimensionality of the manifold to vary from one point to another. This ...
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1answer
36 views

How to recover primal problem from its dual counterpart

I am asking this from context of optimization in machine learning. We often talk about a primal problem and how this primal problem can be solved by first converting it into a dual problem (Using ...
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1answer
441 views

What is the difference between Gaussian discriminant analysis and logistic regression? [duplicate]

As stated in this course pdf, Gaussian Discriminant Analysis (GDA) can also be expressed in the form of $\frac{1}{1+\exp(-\theta^Tx)}$, where $\theta$ is some appropriate function of $φ$, $\Sigma$, $...
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3answers
963 views

More data, to counteract overfitting, results in worse validation accuracy

I am currently trying to classify clothes for my final project in school. My problem is that after I gathered more data, to counteract overfitting, the validation accuracy dropped from 60% to 45%. ...
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1answer
366 views

Regression - mapping an unordered set of tuples to a scalar

I have a problem which I am trying to fit into a classical regression/learning framework. I have a dataset $D$ where each instance $d_i$ is a set of $(x,y)$ pairs, where $x$ is a non-negative number ...
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0answers
12 views

What is the relationship between stochastic mirror descent and stochastic gradient descent?

I don't know much about stochastic mirror descent and was wondering if someone could briefly summarize it in general terms and compare/contrast it to stochastic gradient descent. When I understand ...
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1answer
23 views

LDA detect new emerging topics

Thanks for stopping by. I have a directional question - I've built a Latent Dirichlet Allocation using Gensims Mallet wrapper. I trained the model once on OldDataSet.csv and measured coherence. I have ...
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1answer
177 views

Dimensionality Reduction of Self Organising Maps

I've probably read any article on dimensionality reduction of Self Organising Maps but just couldn't fully comprehend this process. My understanding so far is: SOM are two-layer networks, ...
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1answer
183 views

Does optimizing the parameters in an exponential smoothing model constitute “learning”?

I'm having an argument at work with a colleague who's saying that we need to use machine learning models instead of the current exponential smoothing models (Holt, Holt-Winters) for demand forecasting,...
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1answer
22 views

Determine if groups have significantly different metric values

I am familiar with using ANOVA to analyze differences among group means. However, I am interested in analyzing differences for metrics other than the mean. Is anyone aware of a statistical test that ...
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1answer
1k views

Gamma as inverse of the variance of RBF kernel

I would like to fix the parameter gamma by using the following heuristic and then select C using GridSearch: taking the inverse of variance of RBF. ...
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17 views

Partial Collinearity in Regression

I had a doubt about the effect of multi-colinearity in regression analysis. I understand if two variables are co-related we cannot disentangle the effects of one from the other on the target variable ...
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2answers
44 views

how to analyze time series data and mark if single data is seasonal or not seasonal

I have data set as shown. It is daily sales data for 4 different product for almost a year. I aggregated the sales of product for each day into . I plotted sales of 4 product as per date and got this ...
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0answers
1k views

Example of Input & output vectors for time series Reccurent Neural Network training?

I've been searching for a while now to find the precise way to feed a Recurrent Neural Network (RNN, LSTM, GRU, ESN, Etc) with time series data with no real success. Here is a question that was close,...
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3 views

Setting bias of output layer for imbalanced datasets

From a blog post from Andrej Karpathy on training neural networks: Initialize the final layer weights correctly. E.g. if you are regressing some values that have a mean of 50 then initialize the ...
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0answers
9 views

how to take a subset of a dataset to fine-tune a neural network?

I would like to build a classifier with 80 000 images and 45 classes. As each epochs takes 1 hour to train, Is there a way to win time by training only a subset of the dataset without lowering the ...
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1answer
388 views

Multiple Response Regression in Spark MLLib

I am trying to do a regression using RandomForests in Spark ML where I have several input variables and would like to predict several responses. Training data would look like ...
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0answers
846 views

Confidence interval of precision / recall and F1 score

To summarise the predictive power of a classifier for end users, I'm using some metrics. However, as the users input data themselves, the amount of data and class distribution varies a lot. So to ...
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5 views

Probability of Incurring Maximum Loss

In online classification one can use mistake bound learning, where one assumes that all $y$ are generated by some target mapping $h^*: \mathcal{X} \rightarrow \mathcal{Y},\,\, h^* \in \mathcal{H}$. ...
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2answers
57 views

Fundamentally, how is “the probability that two randomly selected samples belong to different classes” intuitively useful in any notion of purity?

The Gini impurity measure is defined by $$\sum_{i=1}^m f_i(1 - f_i)$$ This based on the probability of two randomly selected samples belonging to two different classes, one of which is $i$, i.e. $...
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0answers
7 views

using latent dirichlet allocation to reduce the number of dimensions in bag of words model?

Does anyone have experience reducing the dimensions in a traditional bag of words model? For example, if you want to train a decision tree on a large set of reviews, the size of the vocabulary ...
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0answers
23 views

Bayesian Linear Regression to Gaussian Process

I'm trying to understand how a Gaussian Process with a squared exponential covariance function can be obtained from Bayesian Linear Regression with a Gaussian prior $N(0,\sigma_p^2 I)$ on the ...
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0answers
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 ...
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2answers
2k views

multiclass classification having class imbalance with Gradient Boosting Classifier

I am using Abalon data for classification from UCI(https://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data). I have scaled data and used TSNE for visualization. ...