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Now we know everything about logistic regression classifiers and
the representation.
What the impact of parameters are, how they can be used for classification,
how they can be used for multi class classification.
That's the foundation we need to talk about linear classifiers and
to do the rest of this classification course.
In this module, we're going to dig in and really figure out
how to learn the parameters for the logistic regression classifier.
But let's start with a quick review just to get us in the same mindset.
Now remember, we had this product reviews or restaurant reviews, and
we wanted to figure out, for example, that the sushi and everything else were awesome
as input has a high probability of being a positive review, while the sushi was good.
The service was okay has a probability of only 0.55 of being a positive review.
In other words, we want to learn a classifier of the form probability of y
given x, where y is the output label positive and negative review and
x is the input sentence, the actual review.
So we talked about this task and we discussed linear classifiers where with
an associate a weight where a coefficient with every input feature,
in our case, would be worse like good, great, awesome and so
on and positive words may have positive coefficients and
negative words may have negative coefficients.
Of the coefficients [INAUDIBLE] words appear in that particular
input centers and that's [INAUDIBLE] from minus infinity to plus infinity.
So, we squeeze that into the line 01 to be able to
predict the probability that the review is positive given
the text of the review and the logistic regression model, that probability is
defined by 1 over 1 plus e to the minus w transposed h.
So we explored this quite a bit in the last module.
Just want to warm us up and
get us into how do we learn these particular coefficients w hat from data.
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