Linked Questions

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0answers
85 views

When to give up CNN? [duplicate]

i'm implementing my first CNN for image classification in a field with very few research. I'm aware i could extract feature and then try SVM, knn... But i want to be sure CNN is not a viable ...
0
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0answers
47 views

Why isn't accuracy of binary classification model improving? [duplicate]

I have a data set with a binary response variable, about 30,000 observations of 8 features, some are continuous and some are categorical. This is an imbalanced data set, the ratio of negatives to ...
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0answers
34 views

Accuracy of RNN getting stuck after 90% [duplicate]

I am using Keras RNN Cell to perform parts of speech tagging. The architecture is as follows(I cannot put the code because of privacy reasons) : An embedding layer of of 40 units of shape (...
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0answers
23 views

Improving accuracy in time series forecasting [duplicate]

I am trying to forecast a very typical sales data. I have tried Arima, ETS, Holtwinters and even neural networks but I can't get a model with more than 40% accuracy [Absolute sum of(forecast-Actual)/...
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0answers
22 views

Acceptable Accuracy, Precision, Sensitivity, and Specificity Thresholds [duplicate]

Are there general rule of thumbs for acceptable accuracy, precision, sensitivity, and specificity values/thresholds in classification? I would imagine that this depends on different applications. I ...
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0answers
21 views

Short string classification, high acc tons of false positives. ¿Are we on the right path? [duplicate]

TL;DR AT THE END Suggested to ask here from https://stackoverflow.com/questions/56038093/short-string-classification-high-acc-tons-of-false-positives-are-we-on-the-ri (same question but on ...
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0answers
21 views

Small sample size with very skewed right response [duplicate]

I have a dataset of 300 observations with 7 predictor variables with 1 continuous response variable. The response is strongly skewed to the right and there are no significant correlations among any ...
0
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0answers
18 views

when to give up in fitting a variable? [duplicate]

I would like to predict a variable from 5 features. I have bad scores (<50%) with all the algorithm I tried (Random Forest, Lasso, SGD, MLP). I would like to quantitatively assess if I should ...
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0answers
16 views

How to Improve the accuracy of my Cancer Prediction Model? [duplicate]

I'm building a logistic regression model to predict if a patient has cancer based on 9 features. Having built learning curves and increasing my regularization parameter to reduce over fitting, which ...
0
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0answers
13 views

Unsatisfactory prediction error - is it possible to improve accurancy? [duplicate]

I'm green in ML field and I try to classify user reports to valid/invalid. My dataset contains of Valid - 7355 samples Invalid - 6285 samples So, I devide data into train and test ...
56
votes
7answers
7k views

Industry vs Kaggle challenges. Is collecting more observations and having access to more variables more important than fancy modelling?

I'd hope the title is self explanatory. In Kaggle, most winners use stacking with sometimes hundreds of base models, to squeeze a few extra % of MSE, accuracy... In general, in your experience, how ...
12
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3answers
2k views

Is my model any good, based on the diagnostic metric ($R^2$/ AUC/ accuracy/ RMSE etc.) value?

I've fitted my model and am trying to understand whether it's any good. I've calculated the recommended metrics to assess it ($R^2$/ AUC / accuracy / prediction error / etc) but do not know how to ...
9
votes
3answers
2k views

Expected best performance possible on a data set

Say I have a simple machine learning problem like a classification. With some benchmarks in vision or audio recognition, I, as a human, are a very good classifier. I therefore have an intuition on how ...
1
vote
2answers
6k views

What is the acceptable level of accuracy while doing Weekly Time Series Forecast

I'm doing a weekly time series analysis and I'm generally getting a mape of 35% is that ok according to the industry standard?
1
vote
3answers
3k views

Running XGBoost with *highly* imbalanced data returns near 0% true positive rate. Tried SMOTE and it did not improve much. What else can I do?

I'm using XGBoost on a dataset of ~2.8M records of hard drive failures, where less than 200 are tagged as failures. After cleaning, there are 11 features in this dataset. Below is my ...

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