It is argued that a statistical model is over-adapted when we provide it with much more data than is needed. To make it fun, imagine trying to fit into oversized clothing. If we take a simple example of linear regression , data training is about determining the Luxembourg Phone Number List minimum cost between the best-fit line and the data points. A few iterations are needed to find out the optimal suitability and reduce costs. Excessive customization occurs here. The line shown in the image above can give a very effective result for a new data point. In the case of conversion, when we run a training algorithm on a data set, we allow to reduce the cost of each iteration. Too long to run will mean lower costs, but noisy data set data will also work. The result will look similar to the chart below. This may seem effective, but it is not. The main goal of an algorithm such as linear regression is to find the dominant trend and adjust Luxembourg Phone Number List the data points accordingly. In this case, however, the row is valid for all data points, which is not important for the efficiency of the model in predicting optimal results for new input data points. Now let’s consider a more descriptive example using the problem statement. Example 2 Problem Statement: Let’s consider whether we want to predict whether a footballer will make a seat at a level 1 football club based on his current performance in a level 2 league. Luxembourg Phone Number List Now imagine teaching and adapting a model with 10,000 such players as a result. When we try to predict the results of the original dataset, let’s say we got 99% accuracy. However, the accuracy of the next dataset is about 50 percent. This means that the model is not well summarized in our training data and unseen data. This is how overcrowding looks. Luxembourg Phone Number List This is a very common problem with machine learning and even data science. Now let’s understand the signal and the noise.