# 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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### Coefficient of correlation between differently spaced time series

I have a timeseries with input flow values in a water plant (measured every 5 minutes) and the meteorological data containing the precipitation in the last 1,3,6,12 and 24 hours (each displayed every ...
8 views

### The multivariate Inverse-Gamma

On wikipedia they give a multivariate form, which to my understanding is used when V is known up until the scaling factor for a Normal-InverseGamma conjugacy. I tried to find a source of the ...
1k views

### Deriving step size/learning rate in the hinge loss passive-aggressive/perceptron algorithm

Recall the perceptron algorithm: cycle through all points until convergence $\text{if }\, y^{(t)} \neq \theta^{T}x^{(t)} + \theta_0\,\{\\ \quad \theta^{(k+1)} = \theta^{k} + y^{(t)}x^{(t)}\\ \}$ ...
171 views

### Machine Learning Models for Classification with Categorical Variables

To start, I'd like to say I have very little experience in machine learning, or statistics/computer science in general. What I am interested in is a list of models I can use to classify a binary ...
1 vote
9 views

### Selecting a test to prove that the observed changes in the performance of two machine learning models are statistically significant

I have developed two machine learning models which I evaluated with two different datasets. My initial hypothesis was that their performance would be higher in dataset 2 as compared to dataset 1. ...
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### Without encoding, how can we solve high cardinality issue?

I already referred the posts here but this question is different. I don't wish to use categorical encoding. details given below I have a dataset of 3000 unique customers purchase data. The dataset ...
1 vote
1k views

### feature selection on training and test data

it is clear that feature selection (FS) have to be done separately on training and then on test data to avoid overly optimistic results. Lets assume that I have training set and test data set. Also ...
6k views

### Is it ok to get negative Cosine Similarity using LSA? [closed]

I am getting negative cosine similarity value between two documents in Latent Semantic analysis. How should it be treated?
1 vote
15 views

### Help with terminology & methodology for a hierarchical (& imbalanced) classification problem

I have a dataset that I am not sure how to analyze, or at least I am not sure of the terms to read up on. I have 25 groups. Each group belongs to one of 3 locations. Each group consists of multiple ...
1 vote
8 views

### Temperature Lag calculation

I am working on a data science project on an industrial machine. This machine has two heating infrastructures. (fuel and electricity). It uses these two heatings at the same time, and I am trying ...
7 views

### For a CNN Neural Network, why do we need to specify the number of nodes in the Conv2D function in Keras? [closed]

As I understand, in CNN, we are only doing dot product calculation on the image in the convolution layer. Below is an example of convolution code. ...
1 vote
95 views

### Calculating Shannon Information of Data Augmentation Strategies

I recently caught Andrew Ng's 2021 talk on MLOps (MLOps: From Model-centric to Data-centric AI). At 26:40, he talks about calculating the effectiveness of cleaning your data (training examples) vs. ...
148k views

### What is the difference between off-policy and on-policy learning?

Artificial intelligence website defines off-policy and on-policy learning as follows: "An off-policy learner learns the value of the optimal policy independently of the agent's actions. Q-learning ...
388 views

### Why does Hutchinson's trace estimator reduce computation complexity?

Given a matrix $A$, we want to compute its trace, in which we can use a trick name Hutchinson's trace estimator \begin{align} tr(A) = tr(A\mathbb{E}[\epsilon \epsilon^T])=\mathbb{E}[tr(A \epsilon \...
27 views

1 vote
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### Neural networks - calculating output manually if $x_1=x_2=0$ . Should this be easy to do?

This is a problem question I'm trying to make sure I understand from a past paper (with no solutions). The R output is below. ...
6 views

### Interpreting Shapley Values on Breast Cancer

I was analyzing Shapley Values on the Wisconsin breast cancer data set (binary classification). I applied it on Random Forest and on Ridge and Lasso Regression. However the summary plot seems to be ...
22 views

### Does using grid search for hyperparemeters make test set redundant?

The purpose of train, validate and test data splits addresses the issue of data leakage when tuning for the model's hyperparameters. Does Grid Search then eliminates the need for test set? Because ...
23 views

### 1D cluster - Jenks optimization - Finding optimal number

I have a sample data variable shown below score 10, 11, 12, 90, 95, 97, 38, 37, 35 Instead of applying/binning data based on ...
11 views

### Assessing importance of interactions between categorical features

The issues with using feature_importance of models such as XGBoost, or even using packages like SHAP or ELI5, is that the results are displayed in a way that doesn'...
19k views

### How do I increase accuracy with Keras using LSTM

I will start with saying I am a complete beginner and doing this assignment for a class, and having some issues on how to get this to be accurate and (somewhat) show it's working! Can someone that ...
348 views

### How to calculate explained variance of Self Organizing Map

Learning SOM recent days, but getting curious how does the explained variance of SOM is calculated. All the articles I have seen ignore this topic. Can anyone give some ideas?
19 views

### "Consensus" on Analyzing Mixed Continuous and Categorical Data in the field of Statistics? [closed]

I have been trying to determine the popular "consensus" as to how mixed continuous and categorical data (e.g. a dataset that has variables on income and gender) is generally analyzed in the ...
71 views

### Feature engineering for sheet music

I have a large dataset of digitized music scores that I'd like to use as input to a network. Initially, I'm looking to train networks to identify key signatures, tempo, dynamics, etc. from the raw ...
1k views

### Using one ml models output to choose another models input

I'm dealing with a low event rate problem (e.g. credit card fraud). I've balanced my data with SMOTE, and ran a neural net model (cross validated with recall as the measure). However my precision (...
9 views

### How can I train Mixture Density Neural Network? [closed]

I am learning Mixture Density Neural Network but it looks different from the usual neural network for regression problem. As far as I have understood from what I have read on the Internet, it gives ...
10 views

### Is it possible to deal with datasets of graphs with different number of nodes in graph nural networks?

I'm dealing with a graph classification problem. In my dataset, each graph has som specific number of nodes. The number of nodes has a range of 1-1000 nodes. At inference time (after training), the ...
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+500

### How to force splits in decision tree to be distributed uniformelly in case of no dependency on feature?

I have targets ordered by a feature. I want to find a single split that minimizes a squared deviation (RMSE). For example, I have 100 values (targets) and it might be the case that, if I split them as ...
267 views

### ML Method for directional forecast

I've uni-variate demand data (Weekly data for 2 years), and wish to do a directional forecast based on the data. Magnitude of the forecast is not important here, but directional accuracy is of ...
12 views

### How do you approach a CNN problem?

I wanna create a machine learning model based on a region based CNN architecture (either RCNN, Fast RCNN or Faster RCNN). As an framework I wanna use Pytorch. I made a image containing apples and ...
56 views
+50

### Are there smooth analytical penalty on leaves sizes for decision trees?

In a decision tree, when we search for an optimal split, we usually minimize root mean square deviation (RMSE). In addition to that we might forbid splits that give too small leaves (for example a ...
1 vote