What Is Overfitting? | Overfitting Explained With Example | Machine Learning Tutorial | Intellipaat
35"This video on ""What Is Overfitting Problem In Machine Learning?"" will alleviate all of your concerns about the overfitting problem that commonly arises while training a model on large datasets. Understand the intuitive notion behind the concept of overfitting, it's statistical and formal definition alongside how you can detect if your model is overfitting. Finally, you will also discover several methods to avoid the problem of overfitting in machine learning. So, let's get started with this machine learning tutorial for beginners!
The topics covered in this tutorial are:
00:00:00 - Introduction to Overfitting
00:00:57 - Building Basic Intuition For Overfitting Problem In Machine Learning
00:04:26 - What Is Overfitting? : Formal Definition
00:08:59 - How to Detect Overfitting?
00:10:50 - How to Avoid Overfitting?
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✅What Is Overfitting?
The overfitting refers to a model that models the training data too well. This 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. This means that the noise or random fluctuations in the training data is picked up and learned as concepts by the model. The problem is that these concepts do not apply to new data and negatively impact the models ability to generalize.
Overfitting is more likely to happen with nonparametric and nonlinear models that have more flexibility when learning a target function. So basically, for these models the amount of characteristics a model will have to learn will be quite substantial. That is the reason why, many nonparametric machine learning algorithms also include parameters or techniques to limit and constrain how much detail the model learns.
✅How to Detect Overfitting?
The main challenge with overfitting is to estimate the accuracy of the performance of our model with the new data. We would not be able to estimate the accuracy until we actually test it, right? To address this problem what we can do is, we can split the initial data set into training and test data sets. With this technique we can actually approximate how well our model will perform with the new data.
✅Different Methods to Avoid Overfitting?
1. Cross Validation
2. Training With More Data
3. Feature Selection
4. Early Stopping
5. Regularization
6. Ensembling
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