Validation-
As we know validation plays a vital role in speech analytics .
Validation process totally depends on the four pointers .
which are as follows-
A true positive(TP) is an outcome where the model correctly predicts the positive class.
A true negative(TN) is an outcome where the model correctly predicts the negative class.
A false positive(FP) is an outcome where the model incorrectly predicts the positive class.
A false negative(FN) is an outcome where the model incorrectly predicts the negative class.
After that we can can apply K -means algorithms cluster technique to get desire result .
What is K- Means Algorithms ?
K-means algorithm is an iterative algorithm that tries to partition the dataset into Kpre-defined distinct non-overlapping subgroups (clusters) where each data point belongs to only one group. In simple words grouping the data set in different sections (According to the requirement) we can understand the concept by below figure also .
In this figure we can clearly see that application of K-Means ,with the help of this we can clustered the data into the group.
In Validation process we also need to understand the concept of Precision and Recall .
So, with help of these mathematical formula we can understand what would be the outcome when you applied it into the model framing .
We must examine precision and recall to evaluate the model, The tricky part is to set the Precision and get recall which are are often in zig zag mode .Improving precision typically reduces recall and vice versa.
In this case we can use approach of A Tug of War with the help of logistic regression .
We can need to set the precision to get the desire result.
Collectively we can use all the approaches to make a good model for validation purpose in speech analytics .
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