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.

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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 ...
EitanT's user avatar
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15 votes
2 answers
12k views

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)...
Aditya's user avatar
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12 votes
3 answers
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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 ...
PicaR's user avatar
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11 votes
7 answers
44k views

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 ...
Harigovind Valsakumar's user avatar
10 votes
2 answers
3k views

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 ...
CanP's user avatar
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10 votes
3 answers
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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 ...
Tom's user avatar
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9 votes
2 answers
17k views

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: ...
Nayana Madhu's user avatar
8 votes
1 answer
1k views

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: ...
zabop's user avatar
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8 votes
2 answers
2k views

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 ...
Xaume's user avatar
  • 182
8 votes
3 answers
690 views

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 ...
Alex's user avatar
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7 votes
6 answers
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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 ...
Lapatrie's user avatar
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6 votes
2 answers
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Image classification architecture for dataset with 710 classes, 90,000 subclasses, and anywhere from 10-1000 images per subclass?

Been struggling with finding the best approach to handle this scenario, I'm also a novice when it comes to machine learning. I have a dataset of around 700 classes, 90,000 total subclasses, and ...
THEOS's user avatar
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6 votes
2 answers
10k views

Encoding before vs after train test split?

Am new to ML and working on a dataset with lot of categorical variables with high cardinality. I observed that in lot of tutorials for encoding like here, the encoding is applied after the train and ...
The Great's user avatar
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6 votes
2 answers
2k views

How Do Machine Learning Models Work and Remember? [closed]

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, ...
osama khan's user avatar
6 votes
3 answers
309 views

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 ...
stevec's user avatar
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6 votes
4 answers
2k views

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 ...
rnso's user avatar
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6 votes
3 answers
268 views

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 ...
Srishti M's user avatar
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5 votes
3 answers
4k views

Should I remove outliers if accuracy and Cross-Validation Score drop after removing them?

I have a binary classification problem, which I am solving using Scikit's RandomForestClassifier. When I plotted the (by far) most important features, as boxplots, to see if I have outliers in them, I ...
ZelelB's user avatar
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5 votes
2 answers
303 views

Modeling uncertainty from known physics

I have an equation given by: $$ \frac{\mathrm{d} s}{\mathrm{d} t}=4a−2s+\lambda(s) $$ where, $a$ is an input constant and $\lambda$ is a non-linear term that depends on $s$. I know that the true ...
user avatar
5 votes
1 answer
6k views

Creating a Object Detection model from scratch using Keras

I have a dataset containing 330 images which contain guns. Along with the images, I have a text file associated with each image file which contains, The number of objects ( guns ) in the image. ...
Shubham Panchal's user avatar
5 votes
3 answers
4k views

Which models can handle null values?

Unfortunately trying to google or research null values in machine learning always brings up pages trying to teach you how to impute the values instead, but I'm trying to find models that can handle ...
user1777900's user avatar
5 votes
3 answers
125 views

Can you learn an algorithm from a trained model?

Are there any papers where an algorithm was entirely based on the results of a trained model? Let me explain. Suppose you want to come up with an algorithm that sorts three numbers $a,b,c$. I can ...
AspiringMat's user avatar
5 votes
2 answers
9k views

How would I apply anomaly detection to time series data in LSTM?

I am using a LSTM RNN in Python and have successfully completed the prediction phase. My ultimate goal is anomaly detection. I'm hoping to have something like what you could see on Facebook Prophet, ...
Ari's user avatar
  • 51
5 votes
2 answers
706 views

How to continue incremental learning when a categorical variable has been assigned additional category labels?

Please help answer this question or point me to any resource. There is a model in an environment where training happens with new data and the data is discarded after training is completed. This keeps ...
Hemant Tiwari's user avatar
5 votes
2 answers
367 views

Model selection: large mean and variance vs small mean and variance

This question was always in my mind. Imagine you are doing 5-10 fold cross validation and one model gives you mean accuracy of 0.8, but with 0.2 standard deviation and the other one gives 0.7 with 0....
WoofDoggy's user avatar
  • 343
5 votes
2 answers
119 views

Practical limitations of machine learning

Working on some applied machine learning problems, I've started to encouter some practical difficulties. Those difficulties relate to - but are not limited to - convergence of the learning process, ...
Lucas Morin's user avatar
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5 votes
1 answer
233 views

When to use Multinomial Naive Bayes?

I am working on a text classification problem, and plan on using Naive Bayes based model. In which cases should I consider using Multinomial Naive Bayes?
Jimmy Collins's user avatar
5 votes
1 answer
98 views

What ML architecture fits fixed length signal regression?

My problem is of regression type - How to estimate a fish weight using a fixed-length signal (80 data points) of the change in resistance when the fish swim through a gate with electrodes (basically 4 ...
Shay's user avatar
  • 51
5 votes
0 answers
54 views

Choosing the right model to learn [closed]

I'm new to the data science world, and I hope to solve a problem using deep learning methods, I started learning how FNN and CNN work and when I saw how many models and methods are the I got a bit ...
Gal Zaidman's user avatar
5 votes
1 answer
280 views

Temporal Aspects in Machine Learning

Concept drift means that the statistical properties of the target variable, which the model is trying to predict, change over time in unforeseen ways. With reference to the classic house price ...
thebluephantom's user avatar
4 votes
3 answers
379 views

What techniques are used to analyze data drift?

I've created a model that has recently started suffering from drift. I believe the drift is due to changes in the dataset but I don't know how to show that quantitatively. What techniques are ...
Connor's user avatar
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4 votes
1 answer
433 views

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

When I want to find a model for my data set, I find that there are lots of algorithms that I can use. I know how to minimize selection choices by separating supervised and unsupervised algorithms and ...
Esi's user avatar
  • 53
4 votes
1 answer
856 views

What happens if at leaf node both classes have same number of samples?

I analyzed a small dataset which had three features, so I kept max_depth of decision tree to be 3, in doing so I found it something intresting, there was a leaf node which had number of samples of ...
Tanmey Rawal's user avatar
4 votes
3 answers
7k views

Alternatives with better GPU than Google Colab Pro

I am currently running/training MAchine learning models that are very GPU expensive, Google Colab Pro is not giving me enough GPU/RAM Is there any alternatives with better GPU and more RAM than ...
The Dan's user avatar
  • 183
4 votes
1 answer
386 views

How can precision be less than one in Leave-One-Subject-Out binary classification if each subject contains only one class

Say I'm trying to classify a medical condition. Theres only two classes: Sick and Healthy. I build a model and I can't split the data because I don't want data from the same patient being in training ...
IsmailE's user avatar
  • 63
4 votes
1 answer
457 views

How to make an MNIST classifier work with blank images?

I am trying to make a Sudoku solver and for the image recognition I trained a CNN but the problem that I am facing is that I don't know how to make it see a clear distinction between numbers and blank ...
Ankit Chawla's user avatar
4 votes
1 answer
590 views

Bi-directionality in BERT model

I am reading the paper BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding that can be found here. It looks to me that the crux of the paper is using masked inputs to ...
Zephyr's user avatar
  • 243
4 votes
3 answers
508 views

How to deal with count data in random forest

I am working on a classification model where my target class is a biased class with the class shape as 0 1 20694 101 Most of my features are the ...
Tushar Mehta's user avatar
4 votes
1 answer
1k views

How to extract characteristics from text using machine learning?

I would like to develop some kind of model/algorithm that allows me to extract the characteristics of a given product name. (let's say the brand, model and color). I am looking for a solution similar ...
Alberto Menendez Romero's user avatar
4 votes
1 answer
2k views

Should I rescale losses before combining them for multitask learning?

I have a multitask network taking one input and trying to achieve two tasks (with several shared layers, and then separate layers). One task is multiclass classification using the CrossEntropy loss, ...
Silver Duck's user avatar
4 votes
2 answers
298 views

Recommender system for next career step

I want to build a recommender system that suggests the next step in your career. About the dataset. About 50'000 Users with following informations: Skills (tags, string value) every job they did (...
Peter's user avatar
  • 59
4 votes
3 answers
42 views

How does one define the possibility space of valid priors (models)?

When one trains a model on data of any complexity one inevitably ends up with a one particular model among a vast many possible models that would make similar (or even, the same) predictions. For ...
MetaStack's user avatar
  • 151
4 votes
1 answer
125 views

Why does a machine learning algorithm need a bias?

The first line of section 2.7.3 in Mitchell's Machine Learning is: "A Learner that makes no prior assumptions regarding the identity of the target concept has no rational basis for classifying any ...
joshuaronis's user avatar
4 votes
2 answers
278 views

What is the best model for a recommendation system using implicit ratings?

I have a similariy matrix that looks like this: I have a bunch of user vectors with 1s and 0s, with a 1 indicating that someone has clicked on an email (as part of a campaign) and zero to indicate ...
Sandy Lee's user avatar
  • 237
4 votes
2 answers
154 views

What is the most appropriate machine learning approach for this scenario?

The scenario is pretty simple, and I'm sure it's been done a million times. The problem is I don't know the terminology to find the correct resources on the web. Scenario: I have an environment that ...
danielbker's user avatar
3 votes
2 answers
282 views

is there a deep learning model that handle 47800+ classes for classification?

I am trying to build a text classifier with 47893 classes and 1.3 billion (1,302,687,947) data samples. What would be the best classifier to build with such kind of data? Each data label will contain ...
Raady's user avatar
  • 239
3 votes
4 answers
997 views

Is there an indicator to know if the predicted value is 100% right?

I am new to Machine Learning. I want to know if there is any indicator which can show us ML's confidence about any given prediction. I am suppose to build an application in which I only want to use ...
meetpd's user avatar
  • 139
3 votes
2 answers
1k views

Uncertainty about shape of ROC curve

I am working on a binary classification and the plotted ROC curves that I am using for evaluation together with AUC, have seemed strange to me. Here is an example. I understand that ROC is a visual ...
lazarea's user avatar
  • 289
3 votes
3 answers
400 views

How to insert two features in a model when a feature only applies to a certain group in the model

I'm building a machine learning model in Python to predict soccer player values. Consider the following feature columns of the dataframe: ...
Caldass_'s user avatar
  • 157
3 votes
3 answers
4k views

How to determine which features matter the most?

I have a large dataset that consists of search results of loans. Someone would input their details like income etc and the results would include a bunch of loans from different companies and different ...
MilTom's user avatar
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