Pythagoras0: /* 위키데이터 */

2021-02-22T07:44:13Z

위키데이터

2021년 2월 17일 (수) 08:12에 Pythagoras0님의 편집

2021-02-17T08:12:54Z

Pythagoras0: /* 메타데이터 */ 새 문단

2020-12-26T12:20:09Z

메타데이터: 새 문단

Pythagoras0: /* 노트 */ 새 문단

2020-12-21T09:57:56Z

노트: 새 문단

새 문서

== 노트 ==

===위키데이터===
* ID : [https://www.wikidata.org/wiki/Q331309 Q331309]
===말뭉치===
# To lessen the chance of, or amount of, overfitting, several techniques are available (e.g. model comparison, cross-validation, regularization, early stopping, pruning, Bayesian priors, or dropout).<ref name="ref_fc8b4642">[https://en.wikipedia.org/wiki/Overfitting Overfitting]</ref>
# Overfitting is more likely to be a serious concern when there is little theory available to guide the analysis, in part because then there tend to be a large number of models to select from.<ref name="ref_fc8b4642" />
# Overfitting/overtraining in supervised learning (e.g., neural network ).<ref name="ref_fc8b4642" />
# If the validation error increases(positive slope) while the training error steadily decreases(negative slope) then a situation of overfitting may have occurred.<ref name="ref_fc8b4642" />
# In fact, overfitting occurs in the real world all the time.<ref name="ref_69cf4749">[https://elitedatascience.com/overfitting-in-machine-learning Overfitting in Machine Learning: What It Is and How to Prevent It]</ref>
# Detecting overfitting is useful, but it doesn’t solve the problem.<ref name="ref_69cf4749" />
# Overfitting is a modeling error that occurs when a function is too closely fit to a limited set of data points.<ref name="ref_7b08f386">[https://www.investopedia.com/terms/o/overfitting.asp Overfitting Definition]</ref>
# However, when applied to data outside of the sample, such theorems may likely prove to be merely the overfitting of a model to what were in reality just chance occurrences.<ref name="ref_7b08f386" />
# As you'll see later on, overfitting is caused by making a model more complex than necessary.<ref name="ref_32cd43eb">[https://developers.google.com/machine-learning/crash-course/generalization/peril-of-overfitting Generalization: Peril of Overfitting]</ref>
# Overfitting happens when a machine learning model has become too attuned to the data on which it was trained and therefore loses its applicability to any other dataset.<ref name="ref_f45dd5ae">[https://www.datarobot.com/wiki/overfitting/ DataRobot Artificial Intelligence Wiki]</ref>
# Overfitting causes the model to misrepresent the data from which it learned.<ref name="ref_f45dd5ae" />
# Picture2 — Regression Example for Overfitting and Underfitting, first Image represents model is Underfit.<ref name="ref_13d4b4b2">[https://medium.com/@itbodhi/overfitting-and-underfitting-in-machine-learning-models-76cb60dbdaf6 Overfitting and Underfitting. In Machine Leaning, model performance…]</ref>
# The opposite of overfitting is underfitting.<ref name="ref_14481710">[https://www.tensorflow.org/tutorials/keras/overfit_and_underfit Overfit and underfit]</ref>
# To prevent overfitting, the best solution is to use more complete training data.<ref name="ref_14481710" />
# As an exercise, you can create an even larger model, and see how quickly it begins overfitting.<ref name="ref_14481710" />
# In this example, typically, only the "Tiny" model manages to avoid overfitting altogether, and each of the larger models overfit the data more quickly.<ref name="ref_14481710" />
# Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data.<ref name="ref_ce551f24">[https://machinelearningmastery.com/overfitting-and-underfitting-with-machine-learning-algorithms/ Overfitting and Underfitting With Machine Learning Algorithms]</ref>
# Overfitting is more likely with nonparametric and nonlinear models that have more flexibility when learning a target function.<ref name="ref_ce551f24" />
# For example, decision trees are a nonparametric machine learning algorithm that is very flexible and is subject to overfitting training data.<ref name="ref_ce551f24" />
# If we train for too long, the performance on the training dataset may continue to decrease because the model is overfitting and learning the irrelevant detail and noise in the training dataset.<ref name="ref_ce551f24" />
# The more we leave the model training the higher the chance of overfitting occurring.<ref name="ref_0815c0db">[https://towardsdatascience.com/what-are-overfitting-and-underfitting-in-machine-learning-a96b30864690 What Are Overfitting and Underfitting in Machine Learning?]</ref>
# Overfitting (or high variance) leads to more bad than good.<ref name="ref_0815c0db" />
# As you probably expected, underfitting (i.e. high bias) is just as bad for generalization of the model as overfitting.<ref name="ref_0815c0db" />
# Depending on the model at hand, a performance that lies between overfitting and underfitting is more desirable.<ref name="ref_0815c0db" />
# Overfitting occurs when you achieve a good fit of your model on the training data, while it does not generalize well on new, unseen data.<ref name="ref_725a978c">[https://towardsdatascience.com/handling-overfitting-in-deep-learning-models-c760ee047c6e Handling overfitting in deep learning models]</ref>
# We can identify overfitting by looking at validation metrics, like loss or accuracy.<ref name="ref_725a978c" />
# There are several manners in which we can reduce overfitting in deep learning models.<ref name="ref_725a978c" />
# Another way to reduce overfitting is to lower the capacity of the model to memorize the training data.<ref name="ref_725a978c" />
# In general there is a trade-off between the size of the space of distinct models that a learner can produce and the risk of overfitting.<ref name="ref_1312c191">[https://link.springer.com/10.1007%2F978-0-387-30164-8_623 Overfitting]</ref>
# As the space of models between which the learner can select increases, the risk of overfitting will increase.<ref name="ref_1312c191" />
# This situation is achievable at a spot between overfitting and underfitting.<ref name="ref_7623d28c">[https://www.geeksforgeeks.org/underfitting-and-overfitting-in-machine-learning/ Underfitting and Overfitting in Machine Learning]</ref>
# If it will learn for too long, the model will become more prone to overfitting due to the presence of noise and less useful details.<ref name="ref_7623d28c" />
# Overfitting is a term used in statistics that refers to a modeling error that occurs when a function corresponds too closely to a particular set of data.<ref name="ref_2b62e1ed">[https://corporatefinanceinstitute.com/resources/knowledge/other/overfitting/ Overview, Detection, and Prevention Methods]</ref>
# Overfitting can be identified by checking validation metrics such as accuracy and loss.<ref name="ref_2b62e1ed" />
# The validation metrics usually increase until a point where they stagnate or start declining when the model is affected by overfitting.<ref name="ref_2b62e1ed" />
# Detecting overfitting is almost impossible before you test the data.<ref name="ref_2b62e1ed" />
# What Is Overfitting In A Machine Learning Project?<ref name="ref_d5b36abd">[https://medium.com/fintechexplained/the-problem-of-overfitting-and-how-to-resolve-it-1eb9456b1dfd The Problem Of Overfitting And How To Resolve It]</ref>
# How Can We Detect Overfitting?<ref name="ref_d5b36abd" />
# Overfitting is when your model has over-trained itself on the data that is fed to train it.<ref name="ref_d5b36abd" />
# These parameters are set to smaller values to eliminate overfitting.<ref name="ref_d5b36abd" />
# There is terminology to describe how well a machine learning model learns and generalizes to new data, this is overfitting and underfitting.<ref name="ref_674055bd">[https://datascience.foundation/sciencewhitepaper/underfitting-and-overfitting-in-machine-learning Underfitting and Overfitting in Machine Learning]</ref>
# Let’s understand what is Best Fit, Overfitting and Underfitting?<ref name="ref_674055bd" />
# Overfitting refers to the scenario where a machine learning model can’t generalize or fit well on unseen dataset.<ref name="ref_674055bd" />
# Overfitting is a term used in statistics that refers to a modeling error that occurs when a function corresponds too closely to a dataset.<ref name="ref_674055bd" />
# In this section we will look at some techniques for preventing our model becoming too powerful (overfitting).<ref name="ref_5aef9701">[https://cnl.salk.edu/~schraudo/teach/NNcourse/overfitting.html Overfitting]</ref>
# Deep learning methodology has revealed a surprising statistical phenomenon: overfitting can perform well.<ref name="ref_5424a00c">[https://www.pnas.org/content/117/48/30063 Benign overfitting in linear regression]</ref>
# The following theorem shows that the kind of overparameterization that is essential for benign overfitting requires Σ to have a heavy tail.<ref name="ref_5424a00c" />
# The phenomenon of benign overfitting was first observed in deep neural networks.<ref name="ref_5424a00c" />
# However, the intuition from the linear setting suggests that truncating to a finite-dimensional space might be important for good statistical performance in the overfitting regime.<ref name="ref_5424a00c" />
# Your model is overfitting your training data when you see that the model performs well on the training data but does not perform well on the evaluation data.<ref name="ref_219c9bad">[https://docs.aws.amazon.com/machine-learning/latest/dg/model-fit-underfitting-vs-overfitting.html Model Fit: Underfitting vs. Overfitting]</ref>
# If your model is overfitting the training data, it makes sense to take actions that reduce model flexibility.<ref name="ref_219c9bad" />
# Overfitting occurs when a model tries to predict a trend in data that is too noisy.<ref name="ref_9ec4c26d">[https://www.kdnuggets.com/2019/12/5-techniques-prevent-overfitting-neural-networks.html 5 Techniques to Prevent Overfitting in Neural Networks]</ref>
# The first step when dealing with overfitting is to decrease the complexity of the model.<ref name="ref_9ec4c26d" />
# This helps in increasing the dataset size and thus reduce overfitting.<ref name="ref_9ec4c26d" />
# So which technique is better at avoiding overfitting?<ref name="ref_9ec4c26d" />
# Example 7.15 showed how complex models can lead to overfitting the data.<ref name="ref_19def9d9">[https://artint.info/2e/html/ArtInt2e.Ch7.S4.html 7.4 Overfitting‣ Chapter 7 Supervised Machine Learning ‣ Artificial Intelligence: Foundations of Computational Agents, 2nd Edition]</ref>
# Overfitting results in overconfidence, where the learner is more confident in its prediction than the data warrants.<ref name="ref_19def9d9" />
# Overfitting a model is a condition where a statistical model begins to describe the random error in the data rather than the relationships between variables.<ref name="ref_90812847">[https://statisticsbyjim.com/regression/overfitting-regression-models/ Overfitting Regression Models: Problems, Detection, and Avoidance]</ref>
# In regression analysis, overfitting can produce misleading R-squared values, regression coefficients, and p-values.<ref name="ref_90812847" />
# I’d really like these problems to sink in because overfitting often occurs when analysts chase a high R-squared.<ref name="ref_90812847" />
# Overfitting a regression model is similar to the example above.<ref name="ref_90812847" />
# Overfitting in Machine Learning is one such deficiency in Machine Learning that hinders the accuracy as well as the performance of the model.<ref name="ref_c134bca9">[https://www.edureka.co/blog/overfitting-in-machine-learning/ What Is Overfitting In Machine Learning? - ML Algorithms]</ref>
# This is what overfitting looks like.<ref name="ref_c134bca9" />
# In order to avoid overfitting, we could stop the training at an earlier stage.<ref name="ref_c134bca9" />
# The main challenge with overfitting is to estimate the accuracy of the performance of our model with new data.<ref name="ref_c134bca9" />
# This example demonstrates the problems of underfitting and overfitting and how we can use linear regression with polynomial features to approximate nonlinear functions.<ref name="ref_abe148bc">[http://scikit-learn.org/stable/auto_examples/model_selection/plot_underfitting_overfitting.html Underfitting vs. Overfitting — scikit-learn 0.23.2 documentation]</ref>
# We evaluate quantitatively overfitting / underfitting by using cross-validation.<ref name="ref_abe148bc" />
# Let’s start with the most common and complex problem: overfitting.<ref name="ref_c6dcacce">[https://allcloud.io/blog/how-to-solve-underfitting-and-overfitting-data-models/ How to Solve Underfitting and Overfitting Data Models]</ref>
# Your model is overfitting when it fails to generalize to new data.<ref name="ref_c6dcacce" />
# It is important to understand that overfitting is a complex problem.<ref name="ref_c6dcacce" />
# The algorithms you use include by default regularization parameters meant to prevent overfitting.<ref name="ref_c6dcacce" />
# What is overfitting in trading?<ref name="ref_ce5d3dd3">[https://algotrading101.com/learn/what-is-overfitting-in-trading/ What is Overfitting in Trading?]</ref>
# Another way to reduce overfitting is by running out-of-sample optimisations.<ref name="ref_ce5d3dd3" />
# Overfitting is a problem in machine learning that introduces errors based on noise and meaningless data into prediction or classification.<ref name="ref_a7344af0">[https://radiopaedia.org/articles/overfitting Radiology Reference Article]</ref>
# Strictly speaking, overfitting applies to fitting a polynomial curve to data points where the polynomial suggests a more complex model than the accurate one.<ref name="ref_a7344af0" />
# There are many techniques to correct for overfitting including regularization.<ref name="ref_a7344af0" />
# Can you explain what is underfitting and overfitting in the context of machine learning?<ref name="ref_42aea1e2">[https://www.analyticsvidhya.com/blog/2020/02/underfitting-overfitting-best-fitting-machine-learning/ Overfitting And Underfitting in Machine Learning]</ref>
# Here’s my personal experience – ask any seasoned data scientist about this, they typically start talking about some array of fancy terms like Overfitting, Underfitting, Bias, and Variance.<ref name="ref_42aea1e2" />
# For example, non-parametric models like decision trees, KNN, and other tree-based algorithms are very prone to overfitting.<ref name="ref_42aea1e2" />
# These models can learn very complex relations which can result in overfitting.<ref name="ref_42aea1e2" />
# The fits shown exemplify underfitting (gray diagonal line, linear fit), reasonable fitting (black curve, third-order polynomial) and overfitting (dashed curve, fifth-order polynomial).<ref name="ref_8e427fa2">[https://www.nature.com/articles/nmeth.3968 Model selection and overfitting]</ref>
# To illustrate how to choose a model and avoid under- and overfitting, let us return to last month's diagnostic test to predict a patient's disease status4.<ref name="ref_8e427fa2" />
# This trend is misleading—we were merely fitting to noise and overfitting the training set.<ref name="ref_8e427fa2" />
# the effects of overfitting become noticeable (Fig. 2b).<ref name="ref_8e427fa2" />
# When you train a neural network, you have to avoid overfitting.<ref name="ref_7f437d22">[https://www.unite.ai/what-is-overfitting/ What is Overfitting?]</ref>
# That’s a quick definition of overfitting, but let’s go over the concept of overfitting in more detail.<ref name="ref_7f437d22" />
# Before we delve too deeply into overfitting, it might be helpful to take a look at the concept of underfitting and “fit” generally.<ref name="ref_7f437d22" />
# Creating a model that has learned the patterns of the training data too well is what causes overfitting.<ref name="ref_7f437d22" />
# Since computation is (relatively) cheap, and overfitting is much easier to detect, it is more straightforward to build a high-capacity model and use known techniques to prevent overfitting.<ref name="ref_817251d4">[https://www.cs.toronto.edu/~lczhang/360/lec/w05/overfit.html overfit]</ref>
# These are only some of the techniques for preventing overfitting.<ref name="ref_817251d4" />
# Since we are studying overfitting, I will artificially reduce the number of training examples to 200.<ref name="ref_817251d4" />
# Focusing on Applicability Domain and Overfitting by Variable Selection.<ref name="ref_af9b86b9">[https://pubs.acs.org/doi/10.1021/ci0342472 The Problem of Overfitting]</ref>
# Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time.<ref name="ref_a8ae4cf5">[https://jmlr.org/papers/v15/srivastava14a.html Dropout: A Simple Way to Prevent Neural Networks from Overfitting]</ref>
# This significantly reduces overfitting and gives major improvements over other regularization methods.<ref name="ref_a8ae4cf5" />
# Ensembling many diverse models can help mitigate overfitting in some cases.<ref name="ref_8580818b">[https://datasciencebowl.com/overfitting/ The Data Science Bowl]</ref>
# So, overfitting in this case is not a bad idea when the number of test set rows (observations) is very large (in the billions) and the number of columns (features) is less than the number of rows.<ref name="ref_8580818b" />
# The best way to avoid overfitting in data science is to only make a single Kaggle entry based upon local CV.<ref name="ref_8580818b" />
# This work exposes the overfitting that emerges in such optimization.<ref name="ref_5b09f82d">[https://www.sciencedirect.com/science/article/pii/S1568494617302806 A study of overfitting in optimization of a manufacturing quality control procedure]</ref>
# Results on two distinct quality control problems show that optimization amplifies overfitting, i.e., the single cross-validation error estimate for the optimized models is overly optimistic.<ref name="ref_5b09f82d" />
# To prevent overfitting, the best solution is to use more training data.<ref name="ref_fa8c1c61">[https://tensorflow.rstudio.com/tutorials/beginners/basic-ml/tutorial_overfit_underfit/ Tutorial: Overfitting and Underfitting]</ref>
# Before we go on to talk about some more simple classifier methods, we need to talk about overfitting.<ref name="ref_e4362328">[https://www.futurelearn.com/courses/data-mining-with-weka/0/steps/25389 Overfitting]</ref>
# That’s a good example of overfitting.<ref name="ref_e4362328" />
# Overfitting is a general phenomenon that plagues all machine learning methods.<ref name="ref_e4362328" />
===소스===
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 == 노트 ==
 ===말뭉치===
 # To lessen the chance of, or amount of, overfitting, several techniques are available (e.g. model comparison, cross-validation, regularization, early stopping, pruning, Bayesian priors, or dropout).<ref name="ref_fc8b4642">[https://en.wikipedia.org/wiki/Overfitting Overfitting]</ref>

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 * ID :  [https://www.wikidata.org/wiki/Q331309 Q331309]

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Pythagoras0: /* 위키데이터 */

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Pythagoras0: /* 메타데이터 */ 새 문단

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