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Questions tagged [accuracy]

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18 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
23 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
90 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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0answers
7 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
21 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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0answers
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
18 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
39 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: $...
2
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1answer
55 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 ...
3
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1answer
50 views

How to creat a plot for the accuracy of a model

Iam pretty new to the whole topic so please dont be harsh. I know these may be simple questions but everybody has to start somewhere ^^ So I created (or more copied) my first little Model which ...
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0answers
35 views

Why does degradation occur in deep neural networks?

It has been shown that "plain" neural networks tend to have an increased amount training error, and accompanied test error, as more layers are added. I am not quite certain as to why this occurs. In ...
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1answer
30 views

CNN for subsets of a dataset - how to tune hyperparameters

I have a dataset and would like to train CNNs on subsets of different size of the dataset. I already have a CNN, which classifies very well if I use the entire dataset. Now the question arises if I ...
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1answer
14 views

Interpreting a curve val_loss and loss in keras after training a model

I am having trouble understanding the curve val_loss and loss in keras after training my model. Can anyone help me understand ...
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1answer
12 views

Sanity check: low PPV but high AUC scores?

I have two algorithms running on a piece of data, both of which perform differently. One of them (call it A) consistently gets a positive predictive value of about 0.75-0.78. Looking at the AUC of ...
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0answers
20 views

Binary training result in chainer

I am training simple Chainer based CNN to recognise MNIST samples. To each sample I add poissonian noise. For the test purpose I have always the same random seed. I restart training resetting the ...
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0answers
123 views

Target mean encoding worse than ordinal encoding with GBDT ( XGBoost, CatBoost )

I have a dataset of 23k rows of an unbalanced dataset 85/15 ratio, 10 variables ( 9 of which are categorical ) , i'm using CatBoost and XGBoost for a binary classification. I applied cv (5 iteration ...
2
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1answer
22 views

How to get the number of steps until a certain accuracy in keras?

I want to see how many steps does it take for my model to reach a certain accuracy.Say 90 percent on cifar10.How can I get this info from the keras model ? EDIT: accuracy in each epoch is accessible ...
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0answers
42 views

30% accuracy for training set, 80% for test set with a 0.3 split

I have a time series dataset on which I am training. For some reason, the training accuracy is 30% while the test accuracy is about 88% after about 10 epochs. Is this at all normal? I should point ...
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0answers
23 views

test accuracy with tensorflow==2.0.0-beta1 vs TensorFlow version: 2.0.0-alpha0

I am trying run same piece of code on both Tensorflow==2.0.0-beta1 and TensorFlow version: 2.0.0-alpha0 In TensorFlow version: 2.0.0-alpha0 I am getting Test accuracy: 85.08% but on Tensorflow==2.0.0-...
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1answer
25 views

Unable to understand the usage of labels argument in sklearn.metrics.f1_score

I am trying to model a dataset with RandomForest Classifier. My dataset has 3 classes viz. A, B, C. 'A' is the negative class ...
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0answers
16 views

How to gauge overfit with MLPClassifier and cross_val_score?

I'm learning sklearn. When using MLPClassifier.fit() and MLPClassifier.predict() I would ...
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1answer
27 views

Random forest with zero precision for unbalanced test data

Apologies if this is a basic question. I have a very unbalanced dataset in which the records are labelled by one of two classes, class1 (negative class) and class2 (positive class): class 1: 1.5 ...
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0answers
9 views

Is veracity in big data theory a good thing to use big data techniques in organization?

I am new to big data field. I know the basic 3V's of it, but I am not understanding the newly added one, which is Veracity. Is it a good thing, which led to taking great analysis from inaccurate data, ...
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2answers
50 views

AUC ROC Threshold Setting in heavy imbalance

I am doing binary logistic regression on a dataset with very heavy class imbalance. Class 1 is only 1% of data. When I train logistic regressor without class weights I get ROC AUC Score of 0.6269. ...
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0answers
19 views

Cost decreases, but accuracy doesn't

It looks like the accuracy is stuck somewhere, I am not sure where and which part is still wrong. Feel free to share your thoughts with me, thanks a lot! :) ...
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0answers
5 views

Does accuracy scale logarthmic with Learningrates?

This is really general, but: How influential is a relatively small change in learning rate for any given algorithm? influences a change from 0.1 to 0.11 in similar magnitude as 0.0001 to 0.1001? I ...
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0answers
30 views

How to describe accuracy/error without ground truth?

I am using machine learning regression models to predict motor scores among a population with spinal cord injury using features derived from their actual movements. Although the clinical measure we ...
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0answers
20 views

The effect of removing pooling layers in the model's accuracy

I know that removing pooling layers will lead to an increase in dimensionality and subsequently, make the training to be more time-consuming. But I'm wondering if ...
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0answers
11 views

Looking at the graphs, what can I do to increase the accuracy of my neural network

I am trying to solve a regression problem using Keras. The data is time series based and I am using one LSTM layer and two Dense layers. Following code shows the basic model setup ...
1
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1answer
109 views

How to know if a model is overfitting or underfitting by looking at graph

Just recently got my hands on tensorboard, but can you tell me what features should I look for in the graph (Accuracy and Validation Accuracy) And please do enlighten me about the concept of ...
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0answers
38 views

Effect Size in comparison of overall accuracy from Random Forest

I would like to compare two overall accuracy statistics (of a Random Forest classifier). My Data: two samples with each containing 25 features and one categorical class variables (9 different ...
3
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4answers
257 views

Why is my test data accuracy higher than my training data?

I'm using four years of data, training on the first 3 and testing on the fourth. Using LSTM w/ Keras. My test data set (which has no overlap at all with the training) is consistently performing better ...
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0answers
26 views

MAPE as an accuracy measure

I want to run and compare time-series forecast methods. Mean Absolute Squared Error (MAPE) is considered one of the strongest metrics for accuracy. My question is the following: If you do $1-MAPE$ ...
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0answers
10 views

Manual setting of target variable based on features' minimum values: f1 score = 1

I am building a classifier for user engagement in my website. Basically, since there are no "proxy" for engagement, i.e. there is no pre-defined target variable, I came up with minimum thresholds ...
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0answers
124 views

How to improve accuracy of a binary classification in neural network

I tried to program a binary classifier to predict whether a customer belongs to one class or another. I have over 200k feature vectors, consisting of numeric 12 features each and assume that should ...
2
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1answer
141 views

Multiclass classification on imbalanced dataset : Accuracy or micro F1 or macro F1

I have a multiclass classification problem. Further, an instance can be assigned to exactly one class. My dataset is highly imbalanced. I know that accuracy is not a good metric to use in this case ...
1
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0answers
28 views

Different accuracy values using the same saved model in tensorflow

I have trained a model in Tensorflow (for some signal classification problem, using mostly convolutional layers, no RNNs), saved It using the callback checkpoints. When I'm testing the said model on a ...
1
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2answers
476 views

Loss value going down while accuracy remains constant?

While I am training, it seems like my loss is going down, but my accuracy remains constant throughout training. It always seems to go towards 0.0023 no matter how I tweak my network, input data length,...
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2answers
394 views

Smaller test data set than training data set in machine learning

I would like to train different machine learning algorithms (SVM, Random Forest, CNN etc.) for the same data set (e.g. MNIST) und then compare their accuracies. The goal would be to find out from ...
2
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2answers
66 views

Accuracy of the model

I'm using this dataset and i'm trying to do logistic regression ...
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0answers
18 views

Accuracy doesn't increase in Binary Classification with 3D coordinates as data

I have 4000 catalogues of galaxies, in each there are 34700 objects, for each of it I have x,y,z coordinates. I want to do a binary classification creating a model which should be able to determine ...
0
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1answer
277 views

Training accuracy decreases

I have a program in which I use sequence to sequence approach as a prediction model with attention. The problem is, while training, the accuracy is always decreasing at each epoch, like shown in the ...
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1answer
92 views

What could cause training CNN accuracy to drop after 7th epoch?

I am training a CNN on some new dataset. Usually, the accuracy steadily improves over 10-20 epochs. I have created a new but similar dataset (using same methods) but now I see a sharp drop after 7th ...
1
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1answer
45 views

Can `k=1` be a good choice for K neighbors classification?

Running sklearn.KNeighborsClassifier() on Kaggle's Leaf Classification sample (set of 99 species, 10 specimen each), with defaults kNN parameters and a grid search ...
3
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2answers
162 views

How to compare paired count data?

I am working with a machine learning approach that counts cars in images. I have a predicted dataset, which is the predicted output from the machine learning approach and a paired "true" dataset, ...
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2answers
863 views

loss/val_loss decrease but acc/val_acc are consistent

I don't know why I am getting such good results. ...
7
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3answers
905 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 ...
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2answers
556 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 ...
1
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2answers
51 views

Classifier performance evaluation

I have an unbalanced dataset which has 920 samples in total, 689 belong to the first class, and 222 to second class. and both classes are significant for me. so when building a classifier model such ...