Questions tagged [model-selection]

Model selection is the process of comparing several models and their respective results to choose the model is best according to some evaluation metric.

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Image-to-image ML problem: one image input and one image output

Overview I'm trying to create a model that takes a "foot heatmap" (input image) and predicts a "shell heatmap" (true heatmap). My data contains foot heatmaps with a corresponding ...
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How to go about predicting administrative fees?

We collect administrative fees from our customers based on many complex business rules albeit based on few variables. I have the history of fees colected through time (about 500 records for each ...
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What are some essential aspects of a data science take-home assignment? [closed]

I've heard about many companies giving job applicants take-home assignments as part of their interviewing process, and I'm interested in what the key aspects of a successful assignment are. I ...
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161 views

How can I choose the best machine learning algorithms from all kinds of algorithms?

I am a beginner at data science and I’ve been learning machine learning for a while with some courses online without any help of a teacher. After I’ve got to work with some real projects on my own, I ...
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how to choose the best machine learning algorithms from all kinds of algorithms? [duplicate]

guys, I am a beginner at data science and I’ve been learning machine learning for a while with some courses online without any help of a teacher and after I’ve got to work with some real projects on ...
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Is it possible to derive anything useful from this piece of data?

Let's say you have online Profile A. Profile A is present on 3 websites: X, Y, Z. ...
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Which regression models should be used with very tiny dataset?

I have a very tiny dataset to make a regression model. only 22 data points with just 2 float features and 1 float output. I want to make models among sklearn ...
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EDA and Attribute selection [closed]

I have a dataset regarding traffic-violations. The attributes are as follows: ...
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What are the most known ML-models that use complex numbers? (if there are any)

Basically just the header. The question is out of curiosity as I haven't seen one yet.
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How can I learn to better explain architectural choices?

I've found out that most of the choices made during model selection are based on a sort of trial and error. From what I've heard, even the most experienced Data Scientists cannot know beforehand ...
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Silhouette Score for different Clustering algorithms

I am trying to compare different clustering algorithms on a dataset and compare the model performance. Since the dataset is quite big (56 features), I applied PCA to reduce the number of features to ...
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average of two models with training set N/2 vs one model with training set N

I'm new to ML and I got a question about training model. Imagine linear regression $Y=\beta^TX+ \epsilon$ and we have training set D (size=N). I have two options: Train model use whole D and we get $\...
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Assess the goodness of a ML generative model (text)

Take a RNN network fed with Shakespeare and generating Shakespeare-like text. Once a model seems mathematically fine, as can be assessed by observing its loss and accuracy over training epochs, how ...
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Selecting Training Set from Model CV Error

What I would like to do is recursively: Train the model on all data Remove the sample(s) with highest error Repeat until the remaining samples have an acceptable error The hypothesis is: "To ...
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Creating a metric to compare models based on score and time

I have several models from which I need to choose the 'best' one. I am trying to find a metric that can define mathematically what 'best' means. The two parameters to be considered are ...
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Why cant we further tune/change the model after evaluating on the test set?

Every thread on stackexchange that I've found says that you can only use the test set once and thats it. So for instance, if you used a linear regression model and got poor results on the test set, ...
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Is it the case that ACF and PACF reflect full information about ARIMA model parameters (p,q)?

Let say i have a single time series of N observations. I'm wondering how informative are ACF and PACF functions of this series. As we know, they can be used to infer orders of AR and MA part of ARIMA ...
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Would all classification models perform similarly in a theoretical and ideal scenario?

Imagine that we count on infinite computation power, an infinite amount of data and we have an infinite amount of time to wait for a model to learn. In such a scenario, we want to have some data ...
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What analytics model/classifier should be used to predict price?

I've go dataset with more than 10 features - football skills describing players. There is also price value in each row. I would like to predict price by specifying ...
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Measure performance of classification model for training on different snapshots

I am trying to do binary classification on some chronological data. Let's assume we have weekly data from the first week of 2017 through the last week of 2020. Now we have found out that 26 weeks of ...
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Which multiclass classification to use for this problem with 9k+ classes?

Need help with which machine learning algorithm/model to use for this problem. The dataset is of product categorization for Amazon. Feature Columns are PRODUCT NAME, PRODUCT DESCRIPTION, BULLET_POINTS,...
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Why are correlation matrices used versus a matrix of R^2 values?

I'm relatively new to DS, so forgive me if this is a dumb question or in the wrong forum When evaluating features it seems that almost everywhere a correlation matrix is used ...
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Ethical consequences of non-deterministic learning processes?

Most advanced supervised learning techniques are non-deterministic by construction. The final output of the model usually depends on some random parts of the learning process. (Random weight ...
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How should I implement machine learning for multi-tenant website?

The company I work for has a website for personal use to track leads and opportunities. I implemented a linear regression algorithm to predict a score for opportunities which is trained on the ...
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How to classify ordered labels(ordinal data)?

I have some data similar to movie ratings and the labels are ordered, like 1 to 10. since the target label is not a nominal but ordinal variable, what types of models should I be using for classifying ...
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569 views

TypeError: __init__() missing 1 required positional argument: 'num_features'

I was trying to denoise image using Deep Image prior. when I use ResNet as an architecture i am getting error. ...
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How can compare suggestion models with different performances?

I have 4 class binary classification models. That models identify which class a particular students is suitable for. For example, we have user 1 and 4 classes ...
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202 views

yolo v4 vs yolo v4-tiny which is better for real time object detection or is that any that is better for real time object detection?

When I am comparing Yolo v4 and Yolo v4 tiny, I notice that the tiny one does significantly worse. It may be caused by the small amount of data I use for testing, so I want to ask in a normal amount ...
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28 views

What is better opensource alternative for identifying small face other than yolo?

I was trying to identify small face meaning that I want to know who that face belong to according to training dataset. I have previously use yolov4 to detect small object before and I know the ...
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Find the relation between two logical models, and their inductive bias

Suppose we want to learn the Boolean function in instance space $X=\{0,1\}^3$. We are given two models to examine: $H_1$ is a set of all logical functions in the conjunctive normal form (CNF), $H_2$ ...
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Applying an algorithm on it's own training data for descriptive purposes?

Good Day, I am newer to data science so I am not confident in this. To set up the question I will describe my data and approach. Data I don't want to share specific data examples as I want to try and ...
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97 views

KFold cross validation ambiguity

I just studied K-Fold cross validation technique for finding model parameters and something seemed to be very confusing. Every tutorial I follow says that for K-Fold validation, the whole dataset will ...
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How to modify a Convolutional Neural Network architecture built for a univariate time series to multivariate time series?

I have built a CNN (in combination with a LSTM cell) that takes 1D time series-like data as an input and performs classification. I am obtaining a good performance, but the complete data has actually ...
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Pretrained model for spectrogram images

I'm working on a sound classification problem. For that, I'm converting the audio signals into spectrogram images and using transfer learning to classify them. Currently I'm using pretrained models ...
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409 views

is it possible to decide model without any data?

Today I just faced a very unique demand from my superior. He asked me whether I can make a model first before we gather the data for training because we don't have any data yet. I was utterly ...
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What are Some Good ML/Deep Learning Approaches to This Type of Problem [closed]

Problem Definition: You are given a dataset of $N$ different features. Most of the features are actually calculations of their own time series of set length (i.e A 5-time step weighted moving average)...
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40 views

Learning Curves and interpretations

I've trained 4 classifiers on an undersampled dataset. I plotted the learning curve for each classifier and I got the following results : I see that for the Log Reg, both curves seem to converge and ...
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Does RandomForest convergence imply I can solve a problem with a NN too?

I'm trying to perform a regression on a dataset, and I've been testing a few models, mostly for practice. I was able to get good results with a RandomForestRegression model, as you can see in the ...
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44 views

Machine Learning Method For Creating Chemical Formula

We work at chemical company . We have nearly 3000 chemical formulas which is composed with chemical raw materials. Our chemical formulas is composed of 20-25 raw materials. As you guess, amount of ...
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113 views

At what stage are ROC curves used when building machine learning model?

When developing a machine learning model, at what stage are ROC curve with AUC used? Typically I have three data sets train - ...
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46 views

Model Selection using Bias Variance Trade Off

I have a Regression Model with Train MAPE as 6% and Test MAPE as 15%. This appears to me as a clear case of over fitting. But can I still use this model assuming 15% Error is not a bad number after-...
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47 views

How do I proceed with model selection?

Only data scientist in an organization and I could really use a sounding board here. In Phase One of a project I deployed four models and served their average as the prediction. I used Random Forest ...
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23 views

Is there a quantitative way to determine if a class of algorithms tends produce low bias or low variance models?

I understand that some machine learning models tend to be low bias, whereas others tend to be low variance (source). As an example, a linear regression will tend to have low variance error and high ...
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How do you, analytically, show you are not using too many features?

One of the managers at my company asked if there is a I could include a metric demonstrates that the my model is not using too many Features/Variables. Is there a metric or best practice that does ...
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How to balance time/effort with transformations, feature selection, and models efficacy in nlp? [closed]

Edit: Question has been edited for reopening (see comment section for justification) Being to new text analytics, I haven't gotten the hang of navigating a typical workflow given the longer times ...
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143 views

Why does a simpler model performs better than a complicated one?

This has happened to me, a complicated model couldn't solve the problem when a simpler one solved it in a few epochs. How is that? I believed that a more complicated model means more number of ...
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23 views

Model selection in active learning

I am dabbling in active learning and was wondering how to combine this in seeking out the best architecture for the network. In my understanding, active learning uses a heuristic for selecting the ...
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Comparing different machine learning methods over multiple test datasets with different number of samples [closed]

Say, I have an image dataset (for example, imagenet) and I am training two image recognition models on it. I train a resnet with 10 layers 3 times on it (each time with different random weight ...
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157 views

model selection in clustering

I am working on a mall customer segmentation dataset (5 features, 200 rows) using clustering. This dataset does not have any ground truth labels. I had a few doubts regarding clustering: Can I use ...
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Is autocorrelation of residuals a problem in machine learning?

Let's assume I have a random forest model and the residuals of the model are autocorrelated. Is this a problem? As an example, let's assume I have two different random forest models, A and B, with a ...

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