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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Bayesian view, Parametric statistics, Non-parametric statistics and SLT

In case you were given a sample dataset $(X_i,Y_i), i \in \{1,..,n\}$ where $X_i \in \mathbb{R}^p$ and $Y = f(X, \Theta) + \epsilon, Y_i \in \mathbb{R} $ where $\epsilon \sim N(0, I)$, how would you ...
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which power of the feature should i train with? regression

I have a explanatory variable (x-axis) and a response variable (y-axis). I am trying to find which power of the feature i should train the dots with. You can ignore the colors for my question. EDIT: ...
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11 views

Discovering peaks/patterns in time-series and clustering them

I have a dataset which contains minute level sensor measurements. Sample is shown here: To me useful information are these peaks in time series, mostly their peak and duration. My idea is to take out ...
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How to Compute the Reconstruction error in Principal Component Analysis at lower dimensions

I have m examples and d features where m<<d. So I managed to compute the eigen value and corresponding its eigen vector ... I want to compute the reconstruction error for various value of ...
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Under what conditions are Maximum Likelihood Estimation and Empirical Risk Minimization equivalent

I've seen some places(such as these lecture notes from ETH Zurich) where they simply declare MLE=ERM, but so far I haven't been able to find any good explanations (or, actually, any explanations at ...
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Question about resulting decision boundary in a classification task

I have 1000 data points from the bivariate normal distribution $\mathcal{N}$ with mean $(0,0)$ and variance $\sigma_1^2=\sigma_2^2=10$ with the covariances being $0$. Also there are 20 more points ...
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29 views

Methods for Improving Random Forest Classification Performance Beyond Hyperparameter Tuning

With the goal of improving out-of-sample performance on a general Random Forest Classification problem, what are other things one can do in addition to tuning a single RFC model's hyperparameters? For ...
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What are SHAP loss values?

Could anybody give a simple explanation of SHAP loss values? Perhaps with an example? (I googled it but didn't find a lot and the things I found were not very clear... at least not to me)
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Overview Feature Extraction in images?

I have been searching for deep feature extraction approaches for a while now, but I did not find a single paper giving me a coarse overview on this matter. Apart from an overview, for example I would ...
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15 views

How to balance transformation decisions, feature selection, and model tuning vs time in text analytics?

Being to new text analytics, I haven't gotten the hang of my typical ML workflow given how long processes take to run in the commonly large feature space of text analytics. I would like to know what ...
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What is use of Tweedie or poisson loss/objective function in XGboost and Deep learning models

I am looking at few competitions in kaggle where people used tweedie loss or poisson loss as objective function for forecasting sales or predicting insurance claims. Can someone please explain the ...
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28 views

merge two neural networks at test time

I am currently using one network which gives me a bad resolution result, so then I use another network to enhance the resolution of my output. My question is: is there an easy way to use both of them ...
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Which model to use? (cross validation with early stopping)

In this example, to keep things simple we use only 1 training and validation set, and we are trying to find the best regularization parameter for ridge regression. The square loss below is on the ...
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Understanding and implementing the dprime measure in Python

According to Wikipedia, the dprime score (aka 'sensitivity index') can be expressed as $$ d' = Z(\text{hit rate}) - Z(\text{false alarm rate})$$ hit rate (aka recall aka sensitivity) and false alarm ...
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DNN underestimates high values

I'm running a DNN on a dataset in order to predict the output values (y). The actual vs fitted graph shows a slight overestimation of the small values and an underestimation of the higher values. The ...
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Which algorithm can I use for breaking down monthly forecast into daily forecasts / buckets?

I'm a trainee at a medical device distribution center. My internship project is to break down monthly forecast into daily forecasts / buckets. in the current situation the monthly forecast is broken ...
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Machine learning strategy for imbalanced data with high number of examples

I am working on a classification problem, with unbalanced classes : Number of positive examples: ~200k; Number of negative ...
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Dimension of Dataset Many to Many one to Many in LSTM keras

My understanding is In Many to Many dimension of if X is(batch_size,timestep,vector_size) and Y is ...
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How does Information Gain work?

I know the algorithm upon which the information gain is based. But how are we sure that after a split, its entropy will always decrease i.e. on dividing the data into smaller chunks, the entropy will ...
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Solution to a single feature logistic regression problem [duplicate]

So I'm having a hard time conceptualizing how to make mathematical representation of my solution for a simple logistic regression problem. I understand what is happening conceptually and have ...
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11 views

What activation and where to use in MaskRCNN RPN

so I've been trying to implement my own version of MaskRCNN, and I am baffled by how the RPN is implemented in various places. Assuming the standard RPN architecture of a shared 3x3 Conv2d, and two ...
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1answer
15 views

K-Means Clustering for telecom customers behavioral usage

I am trying to run K-means clustering on a dataset of 100k records and 26 columns. My problem is in the visualization or plotting clusters part. Since I have several features, I couldn't specify the x ...
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7 views

Basic Questions on Stacking (ensemble models)

I found a paper online "Popular Ensemble Methods: An Empirical Study" (Opitz, Maclin, 1999). Was this really the first observed use of "model stacking" (ensemble learning) in ...
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Graph convolution network for variable number of nodes

Is it possible to train a graph convolutional network on graphs with a varying number of nodes? I have a dataset of graphs with a range of 400-1000 nodes, though I could see a higher number of nodes ...
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How to calculate accuracy semi-supervised (self-training)

Self-training is a wrapper method for semi-supervised learning. First, a supervised learning algorithm is trained based on the labeled data only. This classifier is then applied to the unlabeled data ...
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When to use rmse vs. rmsle

In regression problems, when to use rmse vs. rmsle as evaluation metrics ? I know one reason is that if we want huge differences to be penalized more, when actual and predicted are themselves high. ...
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Hyperparameter tuning vs weight tweaking in Cross-Validation: should I consider 2 different validation sets?

Let's say I have 1000 Samples and want to build an ANN. Then I split my dataset into train set (800) and test set (200). After that, I do the following Cross-validate my train set with different ...
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Why do CNNs work well with natural data such as speech, images, and text?

According to the universal Approximation theorem, we can approximate any given function with two-layer neural networks with a sufficient number of nodes. Then Why do CNNs work well with natural data ...
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1answer
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ReLU outperforming Softplus

I have noticed that PyTorch models perform significantly better when ReLU is used instead of Softplus with Adam as optimiser. How can it happen to be that a non-differentiable function is easier to ...
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6 views

How to define loss function for Discriminator in GANs?

To train the discriminator network in GANs we set the label for the true samples as $1$ and $0$ for fake ones. Then we use binary cross-entropy loss for training. Since we set the label $1$ for true ...
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FInding stationary points of Logistic regression with cross-entropy loss

Let's say that I want to find the stationary points of the Cross-Entropy Loss function when using a logistic regression The 1 D logistc function is given by : \begin{equation}\label{eq2} \begin{split} ...
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32 views

Gibbs sampler of a generative model

I understand what a Gibbs sampler is and I understand how LDA does classification. But I'm unsure how I can generate a Gibbs sampler for an LDA model and how to meld the two concepts. Let's say I ...
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What should be the ratio of number of classes to number of instances per class?

I am trying to train a CNN model for the classification of 100 different classes. I have about 275 instances for each class and there are about 1000 features. While I trained the model by tuning ...
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1answer
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Cross-validation for hyperparameter tuning

I've read as many topics regarding hyperparameter tuning as I could, and I developed the following algorithm for hyperparameter tuning & final model building Split the data in train set (80%) &...
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The validation set approach will tend to select smaller values for k than 10-fold cross-validation for k-nn regresion

The validation set approach will tend to select smaller values for k than 10-fold cross-validation for k-nn regresion. Is this statement true? Why?
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Similarity between graphs of different sizes

I have two graphs G and G' (of different sizes) and I want to check how similar they are. I have read that the Wasserstein distance is used in this case. How can I use it? In scipy there is the ...
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157 views

What does it mean that a dataset is “biased”?

What does it mean when people within the field of Machine Learning talk about biased datasets? I thought it was only estimators that could be biased. In documenting work I have done, I am being asked: ...
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24 views

Interpreting RMSE and MAE

When some models state the MSE or MAE as some value, is that value in the same unit as our target variable and is it for the total model or only an observation? For example, if a model says the MAE is ...
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7 views

ML model type for making multi-step time-series predictions

Consider the following problem: making a prediction for 1 month based on 5 years of stock close prices. What would be the best choices in terms of model structure for this problem? I have considered ...
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Regress 4 values based on an input matrix with shape of (16,5)

Problem: I need to regress 4 values (which quantify how much a user likes a specific topic). I am performing simulations, so I know the ground truth (the real 4 values of the preferences) Input Data ...
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Faster RCNN for one class in object detection

Let say I have a task to detect the bounding box of one object only. And the only thing I care about is the IoU between prediction and ground truth, no need for real-time. My question: Should I ...
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4 views

ML training and test data in making multi-step time-series predictions

Consider the following problem: making a prediction for 1 month based on 5 years of stock close prices. What would be the test data in this problem? The 1 month I would make a prediction for, or ...
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1answer
42 views

Maximum Entropy Discrete Distribution

In Pattern Recognition and Machine Learning the author uses Lagrange multipliers to find the discrete distribution with maximum entropy. Entropy is defined by; $$H=-\sum_i p(x_i)\ln(p(x_i))$$ and the ...
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16 views

Training/test splits (Monte Carlo sensitivity analysis) or Cross-validation

I am using SVM in Matlab (fitcsvm function) to train a classifier for a problem with two classes. Further, I have three features, e.g. A1, A2 and A3, available for each observation composing my full ...
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If base classifier is stable then error of ensemble is caused by bias in base classifier. Why?

I'm reading the book- Intro to Data Mining by Pang-Ning Tan. Under "Bagging" it's written: If a base classifier is stable, i.e., robust to minor perturbations in the training set, then the ...
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Are there defined categories of features in images? [closed]

I have been looking into deep anomaly detection and I am currently wondering about what kind of features can be extracted from an image. I have seen papers about edge features and texture features, ...
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Minimizing The Expected Loss Of a Classification Problem

I found the following squared-loss function for the regression of classification problem: I learned that the expected loss of any classification problem is given by the following: $\begin{gather*} ...
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ML data leaking subtlety

On a 2 class classification problem (think spam detection, with some spam (positive), but a lot more non-spam (negative)), is it OK to share some negative samples between the validation and the ...
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23 views

Design a COVID-19 detection system from students’ health and other information [closed]

I have an assignment to design a COVID-19 detection system from students’ health and other information. But I need some idea to how to do it. Here is the question: Suppose, you are a member of a team ...
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1answer
33 views

What to do when a neural network cannot overfit one training sample? [closed]

Other questions have addressed what to do when a network does not reach good performance on a (medium / big) training set or that overfitting one training sample requires enough capacity. However, ...

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