Questions tagged [accuracy]

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33 views

Why do decision trees have low accuracy?

It seems to be generally acknowledged that decision trees have low prediction accuracy. Is there a concise explanation for why they have low accuracy? I've read this so much, I've accepted it to be ...
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1answer
44 views

scikit-learn RandomForestClassifier always hits 100% test accuracy

I have been playing with a toy problem to compare the performance and behavior of several scikit-learn classifiers. Brief, I have one continuous variable X (which contains two samples of size N, each ...
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10 views

jumpy validation accuracy graph

helo, i have a very weird problem. my validation accuracy graph is very jumpy, and i dont know how to fix it this is the graph: this is a multi label problem. i calculate accuracy with 0.5 threshold ...
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1answer
14 views

Best way to evaluate performance for my case

I have dataset that looks like this ...
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2answers
80 views

Is it better to have higher train accuracy with lower test accuracy or higher test accuracy with lower train accuracy?

The results from my RandomForest model with 5 max features are as follows: 84% train accuracy 76% test accuracy The results with 10 max features: ...
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Since is not possible test all the possible combination, what is the correct procedure to follow on building Machine Learning?

Sorry, I'm a little confused and this is a general question. How can I be sure that the procedure that I am following is the correct one? Following the steps for building a machine learning model, we ...
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21 views

U-Net: Accuracy drops to zero - How do I prevent this from happening?

I have a problem, when training a U-Net. When starting the Training, the Accuracy increases and the loss steadily goes down. At epoch 40, in my example, the validation loss jumps to the maximum and ...
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0answers
20 views

Classification Model showing different accuracy for SAME data?

This is my first post here, so kindly pardon any commonplace errors. So, i have been training an XGBoost multi-class model on Google Colab. I am using a balanced dataset, with 31000 rows, where each ...
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31 views

What is mAP in object detection?

I have been reading through this blog in order to find what mAP is .In the sub heading of AP, they give the example of 5 apple images and finding out the average precision.As far I understand false ...
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11 views

How to set anomaly threshold depending of predictive model accuracy

Say I have a variable with a standard deviation STD I have a predictive model to predict variable. The model accuracy is 80% An anomaly is raised if difference (predicted_value - actual value) > ...
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Is my model over fitting or not?

I have 50000 observations with 70% positive and 30% negative target variable. I'm getting accuracy of around 96-99% which seems unreal of course and I'm worried that my model is over-fitting which I ...
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1answer
41 views

Is there any way to calculate the true,false positives and negatives for a regression problem

I am trying to predict the glucose values of the patients for example values like 45,256,115 etc. based on some features. Currently I am calculating the accuracy in means of RMSE,MSE,R². Is there any ...
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1answer
30 views

Same validation accuracy, different train accuracy for two neural networks models

I'm performing emotion classification over FER2013 dataset. I'm trying to measure different models performance, and when I checked ImageDataGenerator with a model I had already used I came up with the ...
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2answers
61 views

is it ok to get 100% accuracy in random forest classifier algorithm?

while i was building the model to predict the performance of machine using the features like OEF,working time,performance/head etc... I splitted the training data using ...
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1answer
22 views

Using standard deviation as a metric for model selection

I'm really getting stuck with overfitting and I'm trying all I can to reduce it. I want't to write a metric to help score models in a cv loop. I'm using 10x5 folds and still getting out of sample ...
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11 views

Can a binary classification model have lower accuracy, but higher F1-score [duplicate]

I am just reviewing some results where someone compared results of two binary classification models and the results state that model M1 achieved higher accuracy ...
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1answer
21 views

Data augmentation in deep training

I'm trying to understand the role of data augmentation and how it can affect the performance/accuracy of a deep model. My target application is a fire classification (fire or not, on video frames), ...
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2answers
18 views

Metrics for same signal

Is there a metric in tensorflow.keras.metrics that counts how many time the predicted output and the real output have the same signal? For example, if the ...
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2answers
28 views

Manual way to draw accuracy/loss graphs

During the training process of the convolutional neural network, the network outputs the training/validation accuracy/loss after each epoch as shown below: ...
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22 views

Advantage of Centralized training in deep learning over distributed training

I know distributed training is the ultimate solution for scalability and solving resource constraints and sometimes distributed training outperforms centralized training in terms of accuracy. But I'm ...
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2answers
18 views

Present results of the best or the last iteration on dev set?

Which is the correct way - presenting the results of the best or the last iteration on the dev set in a paper? In research papers I usually see only one value, is it the best iteration of all? I'm ...
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8answers
6k views

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 ...
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1answer
38 views

Binary Clasification Accurary

newbie speaking: Commonly we can say that accuracy is defined as total positive/ total nbr cases. But I read that, when it is a binary classifier we should consider: TP+TN/ total nbr cases. Can ...
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1answer
28 views

Making sense of a accuracy plot for a 5 fold training using random forest

I'm using sklearn.model_selection.learning_curve for 5 fold training of data. The code is as given below. ...
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2answers
338 views

macro average and weighted average meaning in classification_report

I use the "classification_report" from from sklearn.metrics import classification_report in order to evaluate the imbalanced binary classification ...
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1answer
21 views

evaluation metrics for multiple values per session

I have an application that executes my foo() function several times for each user session. There are 2 alternate algorithms that i can implement as "foo" function and my goal is to evaluate them based ...
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0answers
27 views

What will cause high accuracy but a big loss?

In the question of What is the relationship between the accuracy and the loss in deep learning?, @Jérémy Blain gave a fantastic interpretation of 'relationship' between accuracy and loss: 1 - low ...
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1answer
67 views

Keras model evaluation accuracy vs. observation

I am a newbie here and trying to make sense out of the scores from model.evaluate from what I am actually seeing in model.predict...
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1answer
33 views

How can calculate Efficiency for predictive models based on accuracy or error over time?

I was wondering if I could express the efficiency of prognostic models according to their accuracy(error, e.g. MAPE or MSE) over time [sec]. So let's imagine I have the following results for different ...
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1answer
69 views

F1_score(average='micro') is equal to calculating accuracy for multiclasification

Is f1_score(average='micro') always the same as calculating the accuracy. Or it is just in this case? I have tried with different values and they gave the same answer but I don't have the analytical ...
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1answer
56 views

Explanation behind the calculation of accuracy in deep learning model

I am trying to model an image segmentation problem using convolutional neural network. I came across code in Github which I am not able to understand the meaning of following lines of codes for ...
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2answers
61 views

Accuracy of KFold Cross Validation for Neural Network

I have a neural network that Im evaluating using 10 -Fold cross validation. The validation accuracy for a fold changes alot during training in the range of -+10% So for example the validation ...
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2answers
49 views

Classification accuracy based on top 3 most likely classifications

My goal is to recommend jobs to job seekers based on their skill set. Currently I'm using an SVM for this, which is outputting one prediction, e.g. "software engineer at Microsoft". However, consider ...
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2answers
44 views

Classification accuracy of a Random Multi-label Classifier

What is the exact accuracy of a random classifier which has n labels (say 1000) where k labels (say 50) are true? Can I say the accuracy of a random classifier has an upper bound of k/n? -Edit- I ...
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0answers
61 views

Prediction error without having a true value

Quick summary about the problem: we are trying to deploy our regression model, where the clients require "individual prediction error". Since we're predicting something unknown in advance, we can't ...
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0answers
28 views

Variability in CNN test results

I'm trying to do some time series analysis on 1-minute forex data using a CNN. I'm new to deep learning and just getting started in building a model. So this is probably a very basic question, but I'...
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2answers
187 views

K fold cross validation reduces accuracy

I am working on a machine learning classifier and when I arrive at the moment of dividing my data into training set and test set Iwant to confron two different approches. In one approch I just split ...
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2answers
33 views

Adding extra variables to XGboost model is worsening the train and test accuracy

I am fitting a multi class model using Xgboost. I am getting an accuracy of 96% on Train and 95% on test. I am using the 80-20 train/test split. However, when I am adding two new features , the ...
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1answer
22 views

Can an R^2, or coefficient of determination be used on non-linear data?

I have used the $R^2$ metric to determine how well my neural network performs a non-linear regression. And it seems to work. The plots look almost identical, and I get an $R^2$ value of 0.93... it ...
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1answer
63 views

Building an efficient feature vector

I am building a classifier for malware analysis, which predicts if I have a malware by looking at the intructions of an assembly code, such as push, mov,... and predicting the optimization method. ...
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1answer
32 views

KNN scoring low compared to Logistic regression in MNIST challenge

KNN gives me a score of 0.76100 while it shows 94% accuracy for my training data (splitted with test_size =0.3) in my jupyter notebook while logistic regression gives me a score of 0.91485 with an ...
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1answer
80 views

The loss and accuracy of this LSTM both drop to nearly 0 at the same epoch

I'm trying to train an LSTM to predict the the Nth token using the N-1 tokens preceding it For each One-Hot encoded token, I ...
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4answers
417 views

Confusion matrix - determine the values of FP FN TP and TN

After running my code ,I get the values of accuracy, precision and recall and I want t determine the values of FP FN TP and TN from these metrics. I tried to calculate it using the formula of each ...
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0answers
18 views

How could a considerable increase in loss leads to an improvement in accuracy?

I'm experimenting with NLP and at the moment, I'm trying to come up with a translator model for converting English sentences to French counterparts. I'm using this dataset (not that it's relevant): ...
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10 views

Cross_validation is decreasing accuracy?

I have certain dataset to train a model. The dataset is not very small in size. First, I split the dataset into training and validation data using traintestsplit (80-20), train the model on training ...
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0answers
22 views

Increase accuracy of occupancy prediction?

I have a project that's aimed to predict the amount of occupants at my local gym given the date and weather. Here's my Kaggle kernel I have two datasets, occupants on a given hour and weather on a ...
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18 views

How to avoid different accuracies when training with subsets?

when trying to train a CNN with randomly selected small subsets (each same size) of the training data set, I get different results in accuracy (the accuracy varies from 0.75 to 0.85). I determine the ...
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0answers
81 views

Pytorch testing/validation accuracy over 100%

So I was training my CNN for some hours when it reached 99% accuracy (which was a little bit too good, I thought). But then it didn´t stop and it went higher than 100%. So I thought, that must be ...
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1answer
333 views

Difference of sklearns accuracy_score() to the commonly accepted Accuracy metric

I am trying to evaluate the accuracy of a multiclass classification setting and I'm wondering why the sklearn implementation of the accuracy score deviates from the commenly agreed on accuracy score: $...
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1answer
561 views

sklearn.accuracy_score(y_test, y_predict) vs np.mean(y_predict == y_test)

What is the difference between these two methods for finding model accuracy? I have used both methods in python3 and i normally get identical results. However in few cases i get completely different ...

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