Determining Feature Importance with Weights
Weights
are values in a machine learning model that adjust the impact each feature has on the outcome as the model processes input data.
Simply put, weights indicate the importance
of each feature.
The larger the weight, the more significant the feature's influence on the result, whereas a smaller weight indicates a lesser impact.
The process of training a machine learning model essentially involves finding the optimal weights.
Understanding Weights with an Example
Let's consider a model predicting a student's test score using features like hours studied
and class participation
.
We can represent the relationship for predicting the test score (exam_score
) using these two features with the following equation:
Here, w1
and w2
are the weights applied to each feature.
If w1
is 0.8 and w2
is 0.2, it implies that hours studied have a greater impact on the test score.
Weights and Neural Networks
In Artificial Neural Networks
, each neuron
has weights.
As the number of connections between neurons increases, so does the number of weights, which are adjusted to detect complex patterns.
In deep learning, for example, weights are tuned across multiple layers
to learn more sophisticated patterns.
Weights that start as meaningless values are improved as training progresses to better reflect the patterns in the data.
In the next lesson, we will delve into the role of Bias
and its distinction from weights.
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