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Overfitting adalah suatu keadaan dimana data yang digunakan untuk pelatihan itu adalah yang "terbaik". Sehingga apabila dilakukan tes dengan menggunakan data yang berbeda dapat mengurangi akurasi (hasil yang dibuat tidak sesuai yang diharapkan). Overfitting dapat terjadi ketika beberapa batasan didasarkan pada sifat khusus yang tidak membuat perbedaan pada data. Selain itu duplikasi data minor yang berlebihan juga dapat mengakibatkan terjadinya overfitting.
Underfitting adalah keadaan dimana model pelatihan data yang dibuat tidak mewakilkan keseluruhan data yang akan digunakan nantinya. Sehingga menghasilkan performa yang buruk dalam pelatihan data. Underfitting terjadi karena model masih mempelajari struktur dari data. Hasilnya, tree bekerja dengan buruk pada masa pelatihan dan tes. Sebagaimana banyaknya node dalam pohon keputusan meningkat, tree memiliki galat pelatihan dan tes yang lebih kecil. Pada saat tree berukuran sangat besar, tingkat terjadinya galat tes mulai meningkat walaupun tingkat galat pelatihannya terus menurun.
Untuk menghindari masalah Overfitting atau Underfitting dapat dilakukan dengan dua pendekatan diantaranya:

Prepruning: Hentikan pembuatan tree di awal. Tidak melakukan pemisahan node jika goodness measure dibawah threshold. Walaupun dapat menyebabkan sulitnya menentukan threshold.
Postpruning: Buang cabang setelah tree jadi. Menggunakan data yang berbeda pada pelatihan untuk menentukan pruned tree yang terbaik.
Untuk mengatasi masalah Overfitting atau Underfitting, terdapat beberapa cara yang bisa dicoba:

Gunakan teknik sampel ulang untuk memperkirakan akurasi model. Dimana nantinya akan melakukan validasi beberapa kali dengan perbandingan data yang berbeda sampai menemukan akurasi yang cukup optimal.
Cek kembali validitas dari set data.




Bacaan lanjutan


Christian, Brian; Griffiths, Tom (6 April 2017). "Chapter 7: Overfitting". Algorithms to live by: the computer science of human decisions. London, United Kingdom: William Collins. hlm. 149–168. ISBN 978-0-00-754799-9.




Pranala luar


Overfitting: when accuracy measure goes wrong - an introductory video tutorial.
The Problem of Overfitting Data
CSE546: Linear Regression Bias / Variance Tradeoff

Kata Kunci Pencarian:

• Overfitting
• Galat generalisasi
• Early stopping (pemelajaran mesin)
• Jaringan saraf konvolusional
• Regularisasi (matematika)
• Bootstrap aggregating
• Filsafat ilmu
• Random forest
• Kurva belajar (pemelajaran mesin)
• Validasi silang
• Overfitting
• Machine learning
• Generalization error
• Early stopping
• Akaike information criterion
• Convolutional neural network
• Statistical learning theory
• Regularization (mathematics)
• Ensemble learning
• Bias–variance tradeoff

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Overfitting - Wikipedia

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Daftar Isi

• how to avoid overfitting in XGBoost model - Cross Validated
• overfitting - What should I do when my neural network doesn't ...
• neural networks - Dealing with LSTM overfitting - Cross Validated
• How does cross-validation overcome the overfitting problem?
• Can imbalance data create overfitting? - Cross Validated
• Random Forest - How to handle overfitting - Cross Validated
• regression - Does over fitting a model affect R Squared only or ...
• How to prevent overfitting in Gaussian Process
• Why is empirical risk minimization prone to overfitting?
• Deep Learning: Why does increase batch_size cause overfitting …

how to avoid overfitting in XGBoost model - Cross Validated

Jan 4, 2020 · $\begingroup$ @dmartin: Thank you for you upvote but apologies as I somewhat disagree with the point you make. . Unless we are looking at a severely imbalanced problem a performance degradation in terms of AUC-ROC from 90% down to 68% is extremely unlikely to be due to "moderate discrepancies" in the train-test (T-T) s

overfitting - What should I do when my neural network doesn't ...

Overfitting is the state where an estimator has begun to learn the training set so well that it has started to model the noise in the training samples (besides all useful relationships). For example, in the image below we can see how the blue on the right line has clearly overfit.

neural networks - Dealing with LSTM overfitting - Cross Validated

Jun 17, 2018 · Your NN is not necessarily overfitting. Usually, when it overfits, validation loss goes up as the NN memorizes the train set, your graph is definitely not doing that. The mere difference between train and validation loss could just mean that the validation set is harder or has a different distribution (unseen data).

How does cross-validation overcome the overfitting problem?

Jul 19, 2020 · As I understand it, overfitting is the result of model selection based on training and testing using the same data, where you have a flexible fitting mechanism: you fit your sample of data so closely that you're fitting the noise, outliers, and all the other variance. Splitting the data into a training and testing set keeps you from doing this.

Can imbalance data create overfitting? - Cross Validated

Jun 18, 2020 · how to prevent overfitting ? On the first point there are several solutions which depends on the use case: keep imbalanced data: the learner knows the class distribution so it knows that class 5 is more likely than class 9. It get the best accuracy on the complete dataset but class 5 will have a great accuracy and 9 will get a poor accuracy

Random Forest - How to handle overfitting - Cross Validated

Aug 15, 2014 · The second treats your training data as if it was a new dataset, and runs the observations down each tree. This will result in an artificially close correlation between the predictions and the actuals, since the RF algorithm generally doesn't prune the individual trees, relying instead on the ensemble of trees to control overfitting.

regression - Does over fitting a model affect R Squared only or ...

Sep 10, 2019 · $\begingroup$ The more regressors that are properly correlated with the output would not lead to overfitting right ? If I used 20 regressors from which 6 are dependent and should be removed, and having R squared equal 1 that is overfitting.

How to prevent overfitting in Gaussian Process

Oct 25, 2018 · To limit overfitting: set the lower bounds of the RBF kernels hyperparameters to a value as high as reasonably possible regarding your prior knowledge try increasing (progressively) the noise kernel, or use sklearn 's alpha parameter in GaussianProcessRegressor (increase the value corresponding to some training points where the GPR seems to ...

Why is empirical risk minimization prone to overfitting?

Apr 7, 2017 · The overfitting of the empirical risk is especially prominent in cases of a small training set. When the data don't contain enough information to learn the underlying pattern, more regularization is needed to fill in the gap. In the specific case …

Deep Learning: Why does increase batch_size cause overfitting …

I used to train my model on my local machine, where the memory is only sufficient for 10 examples per batch. However, when I migrated my model to AWS and used a bigger GPU (Tesla K80), I could accomodate a batch size of 32. However, the AWS models all performed very, very poorly with a large indication of overfitting. Why does this happen?