Overfitting and underfitting – Introduction
Scientists and developers have made tremendous progress in machine learning and artificial intelligence. Machine learning uses data and algorithms to replicate how humans learn and gradually improves the accuracy of the program. The algorithms produce unsatisfactory results because of overfitting and underfitting. You can improve your model’s performance if you understand why errors occur. In this blog, we explore overfitting and underfitting in machine learning and learn how to avoid them.
What is overfitting in machine learning?
In this section, we focus on what is overfitting in machine learning. Overfitting is the undesirable tendency in machine learning models that predicts outcomes accurately for training data and not for new data. Data scientist train their models on known data sets before they use them to make predictions. The model tries to make accurate predictions for fresh data sets based on the training or knowledge. An overfit model does not perform for new incoming data and can give inaccurate forecasts.
What is the catalyst of overfitting?
Before you can learn how to prevent overfitting, it is critical to understand what is the catalyst for overfitting. Overfitting happens when the model cannot generalize but tries to fit too closely with the training dataset. Common causes of overfitting are –
- The data lacks enough samples to represent all possible input data values.
- The training data contains a significant amount of irrelevant data or noisy data.
- The model is trained excessively on a single sample data set.
- Because of the high level of complexity involved, the model learns the noise within the training data.
How to improve overfitting in neural networks?
Overfitting in neural network training is a common problem. When you feed new data to the network, the error is much larger than when you use the training set. Although the network has learned the training instances by heart, it has not learned generalization to new circumstances.
Neural networks are susceptible to overfitting because they learn several parameters when creating a model. Due to its substantial capacity, a model with many parameters can overfit the training data.
The network becomes less prone to overfitting by deleting some layers or decreasing the number of neurons since the overfitting-contributing neurons are eliminated or turned off. Because there are fewer parameters for the network to learn, it cannot memorize all the data points and must learn to generalize.
How to reduce overfitting?
Here are some techniques to reduce overfitting
- Train the data more -You can prevent overfitting by training your data. As you input more training data into the model, it will overfit all the data forcing it to generalize to produce results. This method is time-consuming and expensive.
- Data augmentation – Data augmentation causes minor changes every time the model evaluates the data. It prevents the model from learning the properties of the data set and is a more inexpensive option.
- Data Simplification – Overfitting can occur if the model is complicated. Data simplification means decreasing the complexity of the data to ensure it does not overfit. Simplifying the data can also make the model faster.
- Ensemble – Ensemble is a machine learning method of combining the prediction of multiple models.
How to avoid overfitting?
Here are some methods on how to avoid overfitting –
- Training with more data
- Removing features
- Early stopping the features
How can you detect overfitting?
The best method to detect overfit models is by testing the machine learning models with more data. You can divide the data into subsets to simplify the training and testing process. Separate the data into two main parts – testing and training sets. The training set represents about 80% of the data and is responsible for training the model. While the test set is approximately 20% of the data and is used to test the accuracy of the data.
By segmenting the dataset, you examine the model’s performance on each dataset to detect overfitting and analyze the effectiveness of the training process. You can measure the accuracy of the two data sets to detect overfitting. The model is probably overfitting if it performs better on the training set than on the test set.
Also read: R vs Python | A complete analysis
In this overfitting example, let us assume you want the model to predict a ball. We provide data input to the model like
- Sphere – This feature checks if the object is spherical.
- Play – This checks if one can play with the object.
- Eat – This checks if one cannot eat it.
- Radius = 5 cm -This checks if the radius is 5 cm or less.
Now suppose you show the model a basketball. After checking all the parameters entered, the model will predict the object is not a ball because of the radius parameter.
What is underfitting in machine learning?
Underfitting in machine learning is used to describe a model’s inability to generalize successfully on the new data because it has not detected or learned patterns in the training data. An underfit model performs poorly on the training set and produces incorrect forecasts. Low variance and high bias lead to underfitting.
What are the reasons for underfitting?
Reasons for underfitting are –
- The model is unable to find patterns from the dataset because the dataset contains noise or outliers.
- The model exhibits a large bias because of its inability to accurately represent the relationship between input variables and a target value and occurs when there is a varied dataset.
- The model considered is too simple.
- Incorrect hyperparameters tuning often leads to underfitting due to under-observing of the features.
What are the techniques to correct underfitting?
Some of the techniques to correct underfitting are –
- Increase the complexity of the model.
- Increase the number of features in the dataset.
- Reduce data noise.
- To get better results, increase the training period or the number of epochs.
How to avoid underfitting?
Here are some ways how to avoid underfitting –
- Increase duration of the training – If you stop training too soon, you can get an underfit model. You can extend the training duration can prevent underfitting. Remember to find a balance between overtraining and underfitting.
- Feature selection – Select particular features with any model to get specific results. In the absence of predictive features, you need to introduce more features or add more significant features. The model will get more complicated and will improve training outcomes.
- Decrease regularization – You can apply regularization to reduce the variance to a model, by applying a penalty to input parameters with a larger coefficient. You can use several methods such as dropout, L1 regularization, and Lasso regularization to reduce noise and outliers. The model is unable to recognize the dominating trend if the data features become too consistent. Adding more complexity and variety by reducing the amount of regularization, which enables you to train the model successfully.
Let us continue with the model to predict a ball. In the underfitting model, you trained the model with only one feature the ball is a sphere. The attribute checks if the object has a spherical shape.
Now after training the model, you check the model with a lemon. As you told the model anything that is a spherical shape is a ball. It will predict the object is a ball. The model failed because we trained it on fewer data (an under-fit model).
Overfitting vs underfitting: overview
Overfitting and underfitting in machine learning are common pitfalls in machine learning that you should avoid. In this section, we look into the difference between overfitting vs underfitting.
Overfitting is an error in deep learning algorithms, where the model tries to match all the training data and ends up retaining the data pattern and noise/random oscillation. The goal of models is defeated because they do not generalize and perform well in the presence of unforeseen data scenarios.
Underfitting occurs when the model is unable to map the input to the target variable. The inaccuracy in the training and unseen data samples increases when characteristics are not observed fully. Unlike overfitting, which occurs when a model performs well on the training set but fails to transfer its knowledge to the testing set.
What is a good fit in a statistical model?
A good fit in a statistical model refers to the quality of the approximation of the target function. This is a great term to use for machine learning because supervised machine learning algorithms aim to estimate the underlying mapping function for the output variables given a set of input variables.
Statisticians frequently employ the goodness of fit, which refers to the metrics used to calculate how closely the function’s approximation matches the target function. The measure of goodness of fit summarizes the discrepancy between the observed value and the expected value for the model being analyzed.
FAQ: Overfitting and underfitting
Poor performance in a machine learning model is either because of overfitting or underfitting the data. The goal of machine language models is to generalize well or provide suitable output for a given set of unique inputs. In this section, we focus on some FAQs: overfitting and underfitting.
What is the catalyst of overfitting?
The catalyst of overfitting is as follows –
- Training data contains noise or garbage values in it.
- The model has high variance.
- The size of the training dataset is insufficient.
- The model is very complex.
What is cross-validation in machine learning?
The process of cross-validation in machine learning involves training multiple ML models on subsets of input data and comparing the results to the corresponding subset of data to assess the performance of the models. Data scientists use cross-validation to identify overreaching or failing to generalize a pattern. Cross-validation involves the following steps –
- Retain a certain amount of the sample data set.
- Train the models by using the remaining data set.
- Use the retained data set to test the model.
What is variance in ML?
Variance in ML refers to the alterations in the model when using different sets in the training data set. Variance is the model’s prediction ability to vary or how much the ML function may change based on the input data set. It occurs when the model is complicated with several features. The model can have high variance low bias or low variance high bias.
What is Bias?
Bias is a phenomenon that alters the results in favor of or against a certain idea. Bias is seen as a systematic error that occurs in the machine learning model because of false assumptions made during the machine learning process. Technically, we can define bias as the difference between the average model prediction and the actual data. It describes how well the model matches the training set like overfit data has high bias.
What is noise in machine learning?
Noise refers to the unwanted behavior within the data that provides a low signal-to-noise ratio. Noise can be thought of as errors in the collection of data. Algorithms can misinterpret noise and start generalizing from it. While it is impossible to reduce all noise in a data set, you can reduce it substantially by understanding its cause and correcting them.
What is signal processing in machine learning?
A signal is a method of communicating information and is a mathematical function. Some examples of signals are – audio, images, ECG, and radar. Electrical engineers use signal processing to model and analyze digital and analog data representations of physical events. Machine learning is seen as an extension of signal processing, where linear processing blocks replace non-linear blocks. This allows users to handle a broader set of problems.
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