Data Science Stack Exchange
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Questions tagged [machine-learning-model]

A machine learning model is a simplified representation of a dataset, derived from statistics in the data, used to make predictions. It can represent patterns, behaviours or features within this dataset which have been learnt by the algorithm during training.

A machine learning model is a simplified representation of a dataset, derived from statistics in the data, used to make predictions. It can represent patterns, behaviours or features within this dataset which have been learnt by the algorithm during training.

830 questions
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What would I prefer - an over-fitted model or a less accurate model?

Let's say we have two models trained. And let's say we are looking for good accuracy. The first has an accuracy of 100% on training set and 84% on test set. Clearly over-fitted. The second has an accuracy of 83% on training set and 83% on test set.…
EitanT
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Why should we use (or not) dropout on the input layer?

People generally avoid using dropout at the input layer itself. But wouldn't it be better to use it? Adding dropout (given that it's randomized it will probably end up acting like another regularizer) should make the model more robust. It will make…
Aditya
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What are the disadvantages of accuracy?

I have been reading about evaluating a model with accuracy only and I have found some disadvantages. Among them, I read that it equates all errors. How could this problem be solved? Maybe assigning costs to each type of failure? Thank you very much…
PicaR
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I got 100% accuracy on my test set,is there something wrong?

I got 100% accuracy on my test set when trained using decision tree algorithm.but only got 85% accuracy on random forest Is there something wrong with my model or is decision tree best suited for the dataset provided. Code: from…
Harigovind Valsakumar
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LightGBM - Why Exclusive Feature Bundling (EFB)?

I'm currently studying GBDT and started reading LightGBM's research paper. In section 4. they explain the Exclusive Feature Bundling algorithm, which aims at reducing the number of features by regrouping mutually exclusive features into bundles,…
Tom
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Why should I understand AI architectures?

Why should I understand what is happening deep down in some AI architecture? For example LSTM-BERT- Partial Conv... Architectures like this. Why should I understand what is going on while I can find any model on the Internet or any implementations…
CanP
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Optimising for Brier objective function directly gives worse Brier score than optimising with custom objective - what does it tell me?

I am training an XGBoost model and as I care the most about resulting probabilities, not classification itself I have chosen Brier score as a metric for my model, so that probabilities would be well calibrated. I tuned my hyperparameters using…
Xaume
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How to Use Shap Kernal Explainer with Pipeline models?

I have a pandas DataFrame X. I would like to find the prediction explanation of a a particular model. My model is given below: pipeline = Pipeline(steps= [ ('imputer', imputer_function()), ('classifier', RandomForestClassifier() …
Nayana Madhu
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Chi-square as evaluation metrics for nonlinear machine learning regression models

I am using machine learning models to predict an ordinal variable (values: 1,2,3,4, and 5) using 7 different features. I posed this as a regression problem, so the final outputs of a model are continuous variables. So an evaluation box plot looks…
Alex
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How could I estimate slope of lines on a scatter plot?

I have a list of coordinate pairs. To the human eye, they form lines with a constant slope: This is how I generated that image above: import numpy as np np.random.seed(42) slope = 1.2 # all lines have the same slope offsets = np.arange(10) # we…
zabop
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Is it advisable to combine two dataset?

I have two datasets on heart rate of subjects that were recorded in two different places (two different continent to be exact). The two research experiments aimed to find the subjects' emotions based on how much their heart rate change over time. I…
Lapatrie
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Least Squares optimization

The cost function given as $\hat{\beta} = (Y - \beta X)^T (Y-\beta X)$ is used to evaluate the weights $\beta$. Here $X$ is the data and $Y$ is the output. On taking the derivative, we get the estimates of the weights. This is a Least Squares…
Srishti M
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How Do Machine Learning Models Work and Remember?

I am new to machine learning. I am confused about how a machine learning model remembers what it learns. And how it learns. I know the basic workflow of machine learning: first is data gathering, then data preprocessing, then creating training and…
osama khan
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What are the individual models within a machine learning ensemble called?

I am aware that an ensemble machine learning model is a stack of two or more machine learning models. Is there a word to refer to those individual models that go into the ensemble model? (i.e. a specific term/jargon?) I wish to refer to them and am…
stevec
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Alternatives to Logistic Regression

I have age, gender, height, weight and some other similar parameters of 15000 subjects. I also have one column showing if they had a medical condition (present in about 20% subjects). I now want to analyze the factors which are most contributing to…
rnso
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