Hello.
Welcome back.
The cause of poor performance in machine learning can be attributed to either over 15 or under 15.
What we want is a model that fits appropriately over fit in happens when a model learns the detail and
the 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 a
concept by the model.
The problem is that these concepts do not apply to new data and negatively impact the modules ability
to generalize and generalization refers to how well the concept learned by a machine learning model
can apply to specific examples not seen by the module when it was learned the goal of a good machine
learning module is to generalize well from the training data to a new data from the problem domain.
This allows us to make predictions in the future on did on the data.
The model has never seen before.
In summary of our Fit and refers to a model that models the training data too well and under fits in
on the other hand refers to a model that can need our model the training data no generalized to new
data.
Ideally we want to select a model at the sweet spot between under 15 and over 15.
This is their goal but it's a bit difficult in practice to achieve without a bit of trial and error.
An example of fitting is when your training set error is 1 percent and your test error is somewhere
around 15 percent.
Unlike sample 4 under fitness when you are training set error is 15 percent and your test set error
is very large as well like 16 percent and humans are able to make the same prediction with a 0 percent
error.
1 in humans perform 15 percent better on the training set and 16 percent better on the test set.
Another name for under 15 is high bias and another name for over 15 is high variance when under 15 occurs
there are three things we can generally do.
We can make our neural network bigger.
We can train for a longer time.
Oh we can also try a different neuro network architecture when over 15 years we can either get more
data apply a method known as regularization or try different neural network architecture so that's all
there is for this lesson and I shall see you in the next lesson.
Have a nice day.