- Q 5: What is overfitting and underfitting?
Answer: Solving the issue of bias and variance is really
about dealing with over-fitting and under-fitting. Bias is reduced, and variance is
increased in relation to model complexity.
As more and more parameters are added to a model, the
complexity of the model rises, and variance becomes our primary concern while
bias steadily falls.
Underfitting: The model has not
captured the underlying logic of the data, it clusmey and low accuracy.
Q 6 : What is Hypothesis?
Answer:
Hypothesis is a predictive
statement, capable of being tested by scientific methods, that relates an independent
variable to some dependent variable.
A hypothesis states what we are
looking for and it is a proportion which can be put to a test to determine its
validity. E.g. Student who receive counselling will show a greater increase in
creativity then students not receiving counselling.
Characteristics of Hypothesis:
- Clear and precise.
- Capable of being
tested
- Stated relationship
between variables.
- Limited in scope and
must be specific.
- Stated as for
possible in must simple terms so that the same is easy understand by all
concerned. But one must remember that simplicity of hypothesis has nothing to
do with it’s significance.
- Consistent with most
known facts.
- Responsive to testing
with in a reasonable time. One can’t spend a life time collecting data to test
it.
- Explain what is
claims to explain; it should have empirical reference.
Null Hypothesis:
-
It is an assertion
that we hold as true unless we have sufficient statistical evidence to conclude
otherwise.
-
Null hypothesis is
donated by H0
-
If a population mean
is equal to hypothesised mean or sample mean, then Hypothesis can be written as
Alternative
Hypothesis:
-
The Alternative
hypothesis is negative of full hypothesis and is denoted by Ha
-
The alternative
Hypothesis can be written as
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