Overfitting In-Depth Lesson II - Overfitting & Underfitting

The Python Codes are available at this link:
πŸ‘‰ https://www.aisciences.academy/ytube-overfitting-underfitting
β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬

In this video, we are going to thoroughly explain what is overfitting.

We will discuss the following in this video:
πŸ•• (0:00:07) Introduction
πŸ•• (0:00:13) Overfitting
πŸ•• (0:02:46) Diagnosis
πŸ•• (0:03:41) Solutions
πŸ•• (0:04:01) Validation Set
πŸ•• (0:06:17) Increase Data
πŸ•• (0:07:56) Less Parameters
πŸ•• (0:11:26) Regularization
πŸ•• (0:14:26) Early Stopping
πŸ•• (0:16:44) Implementation

#data-science #artificial-intelligence #python #developer

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Overfitting In-Depth Lesson II - Overfitting & Underfitting
Kennith  Kuhic

Kennith Kuhic

1623383040

Understanding Overfitting and Underfitting for Data Science A

Whenever a data scientist works to predict or classify a problem, they first detect accuracy by using the trained model to the train set and then to the test set. If the accuracy is satisfactory, i.e., both the training and testing accuracy are good, then a particular model is considered for further development. But sometimes, models give poor results. A good machine learning model aims to generalize well from the training data to any data from that domain. So why does this happen? Here comes the major cause of the poor performance of machine learning models is Overfitting and Underfitting. Here we walk through in detail what is overfitting and underfitting and realizing the effect through Python coding and lastly, some technique to overcome these effects.

The terms overfitting and underfitting tell us whether a model succeeds in generalizing and learning the new data from unseen data to the model.

Brief information about Overfitting and Underfitting

Let’s clearly understand overfitting, underfitting and perfectly fit models.

From the three graphs shown above, one can clearly understand that the leftmost figure line does not cover all the data points, so we can say that the model is under-fitted. In this case, the model has failed to generalize the pattern to the new dataset, leading to poor performance on testing. The under-fitted model can be easily seen as it gives very high errors on both training and testing data. This is because the dataset is not clean and contains noise, the model has High Bias, and the size of the training data is not enough.

When it comes to the overfitting, as shown in the rightmost graph, it shows the model is covering all the data points correctly, and you might think this is a perfect fit. But actually, no, it is not a good fit! Because the model learns too many details from the dataset, it also considers noise. Thus, it negatively affects the new data set; not every detail that the model has learned during training needs also apply to the new data points, which gives a poor performance on testing or validation dataset. This is because the model has trained itself in a very complex manner and has high variance.

The best fit model is shown by the middle graph, where both training and testing (validation) loss are minimum, or we can say training and testing accuracy should be near each other and high in value.

#developers corner #data science machine learning ai #machine learning underfitting #overfitting #underfitting

Overfitting In-Depth Lesson II - Overfitting & Underfitting

The Python Codes are available at this link:
πŸ‘‰ https://www.aisciences.academy/ytube-overfitting-underfitting
β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬β–¬

In this video, we are going to thoroughly explain what is overfitting.

We will discuss the following in this video:
πŸ•• (0:00:07) Introduction
πŸ•• (0:00:13) Overfitting
πŸ•• (0:02:46) Diagnosis
πŸ•• (0:03:41) Solutions
πŸ•• (0:04:01) Validation Set
πŸ•• (0:06:17) Increase Data
πŸ•• (0:07:56) Less Parameters
πŸ•• (0:11:26) Regularization
πŸ•• (0:14:26) Early Stopping
πŸ•• (0:16:44) Implementation

#data-science #artificial-intelligence #python #developer

Myriam  Rogahn

Myriam Rogahn

1599397140

Solving Underfitting and Overfitting

Underfitting_ and overfitting are both common problems data scientists come across when evaluating their model. It is important you are aware of these issues and what we can do resolve them._

Definitions

**Underfitting: **Occurs when our model fails to capture the underlying trend in our data:

Image for post

Models which** underfit **our data:

  • Have a Low Variance anda High Bias
  • Tend to have **less features **[ π‘₯ ]
  • High-Bias: Assumes more about the form or trend our data takes
  • Low Variance: Changes to our data makes** small **changes to our model’s predicted values

β€” β€” β€” β€” β€” β€” β€” β€” β€” β€” β€” β€” β€” β€” β€” β€” β€” β€” β€” β€” β€”

**Overfitting: **Occurs when our model captures the underlying trend, however, includes too much noise and fails to capture the general trend:

Image for post

Models which overfit our data:

  • Have a High Variance and a Low Bias
  • Tend to have many features [π‘₯, π‘₯Β², π‘₯Β³, π‘₯⁴, …]
  • High Variance: Changes to our data makes **large **changes to our model’s predicted values.
  • Low Bias: Assumes less about the form or trend our data takes

β€” β€” β€” β€” β€” β€” β€” β€” β€” β€” β€” β€” β€” β€” β€” β€” β€” β€” β€” β€” β€”

**A Good fit: **Does not overfit or underfit our data and captures the general trend of our data:

Image for post

Models which fit our data well:

  • Have a Low Variance and Low Bias
  • Tend to have a reasonable number of features
  • Perform well on test data [new data given to the model]

#overfitting #underfitting #artificial-intelligence #data-science #machine-learning

Noah  Rowe

Noah Rowe

1596969120

Underfittedβ€” Generalizedβ€Šβ€”β€ŠOverfitted

A brief note on how bias and variance makes a model as Underfitted or Generalized or Overfitted!

In this post, instead of writing so many paragraphs I just made an info-graphic for ease of understanding.

Image for post

Underfitted β€” Generalized β€” Overfitted

Underfitted:

A model could fit the training and testing data very poorly (high bias and low variance) β€” left most graph in above Info-graphic. This is known as underfitted.

Overfitted:

A modelcan fit the training data very well and the testing data very poorly. (low bias and high variance) β€” Right most graph in above Info-graphic. This is known as overfitted.

#underfitting #bias #machine-learning #overfitting #variance #deep learning

Sofia  Maggio

Sofia Maggio

1624010400

What are Overfitting and Underfitting in Machine Learning?

What are overfitting and underfitting? it frequently comes when you are training and testing a machine learning model or a deep learning model. If you are going to build a well-generalized model then it should not be gives overfitting and underfitting situations.

What is a well-generalized model? It means your model has trained well and also performs well to your unseen data (testing data). So a well-generalized model gives less training error and less testing error compared to overfitting and underfitting models.

When talking about overfitting and underfitting, it has some basic terms which are needed to be understood to get a clear idea about the whole thing

  1. Signal = This refers to data that help to identify general patterns to a model
  2. Noise = This refers to data which are having some special cases for the particular object in the dataset.

As an example, if we want to predict an animal as a bird or not we know having feathers is a common feature for a bird there for it is a signal. but having a high weight is not a common feature (ostrich). therefore a model has to especially noticed that feature and it is not following the common features set. it calls as noise.

3. Bias = This refers to the error. It means a model not perform well and the predicted value and true value have a huge difference.

4. Variance = This refers to the variability of model output. this is what happens when a model performs well for training data but poorly performs for testing data.

Overfitting

Overfitting mainly happens when model complexity is higher than the data complexity. it means that model has already captured the common patterns and also it has captured noises too. It is like that model has covered all of the data points exactly even it has not avoided inaccurate data points in dataset.

#overfitting #deep-learning #machine-learning #underfitting