Questions tagged [accuracy]

In data science, accuracy is a measurement used to determine which model is best at describing the underlying patterns of a dataset.

Filter by
Sorted by
Tagged with
43
votes
4answers
19k views

Is it always better to use the whole dataset to train the final model?

A common technique after training, validating and testing the Machine Learning model of preference is to use the complete dataset, including the testing subset, to train a final model to deploy it on, ...
36
votes
8answers
7k 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 ...
11
votes
2answers
14k views

Validation vs. test vs. training accuracy. Which one should I compare for claiming overfit?

I have read on the several answers here and on the Internet that cross-validation helps to indicate that if the model will generalize well or not and about overfitting. But I am confused that which ...
9
votes
5answers
13k views

In which epoch should i stop the training to avoid overfitting

I'm working on an age estimation project trying to classify a given face in a predefined age range. For that purpose I'm training a deep NN using the keras library. The accuracy for the training and ...
92
votes
4answers
92k views

Advantages of AUC vs standard accuracy

I was starting to look into area under curve(AUC) and am a little confused about its usefulness. When first explained to me, AUC seemed to be a great measure of performance but in my research I've ...
2
votes
2answers
4k views

loss/val_loss are decreasing but accuracies are the same in LSTM!

I am trying to train a LSTM model, but the problem is that the loss and val_loss are decreasing from 12 and 5 to less than 0.01, but the training set ...
2
votes
1answer
2k 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 ...
11
votes
3answers
5k views

Inverse Relationship Between Precision and Recall

I made some search to learn precision and recall and I saw some graphs represents inverse relationship between precision and recall and I started to think about it to clarify subject. I wonder the ...
15
votes
3answers
28k 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 ...
5
votes
1answer
2k views

Training and cross validation error curves

I have a graph which plots training datasize on X axis and accuracy on y axis. I plotted the curves using sklearn's learning_curve. It is observed that the accuracy of training dataset decreases but ...
7
votes
3answers
2k views

0.1 accuracy on MNIST fashion dataset following official Tensorflow/Keras tutorial

My goal is to classify products pictures into categories such as dress, sandals, etc. I am using the MNIST fashion dataset, following this official tutorial word-per-word: https://www.tensorflow.org/...
5
votes
2answers
280 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 ...
3
votes
1answer
182 views

Understanding ROCs in imbalanced data-sets

A response variable (label) $B$ can either be $0$ or $1$. In the training set, $B_i = 1$ is an extremely rare event at only $0.26\%$ occurrences. Which makes the prediction of this label on a test ...
7
votes
4answers
5k views

Log loss vs accuracy for deciding between different learning rates?

While model tuning using cross validation and grid search I was plotting the graph of different learning rate against log loss and accuracy separately. Log loss When I used log loss as score in ...
2
votes
1answer
9k views

Accuracy for Kmeans clustering

I am looking for accuracy python code for kmeans clustering with no labels. Is there anyone who knows about it? it is ok that is not built-in function. Manually made is also ok
1
vote
1answer
97 views

Hyperparameter tunning for Random Forest- choose the best max depth

I'm trying to choose the best parameters for random forest model. For that goal I hae run my model in loop with only one parameter and each time I have changed the number for the parameter max depth. ...
0
votes
2answers
77 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 ...