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Probability

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The probability that it will rain tomorrow is dependent on whether or not it is raining today and whether or not it rained yesterday.

If it rained yesterday and today, there is a 20% chance it will rain tomorrow. If it rained one of the days, there is a 60% chance it will rain tomorrow. If it rained neither today nor yesterday, there is a 20% chance it will rain tomorrow.

Given that it is raining today and that it rained yesterday, write a function <code>rain_days</code> to calculate the probability that it will rain on the nth day after today.

Example:

Input:

<pre tabindex="0" class="chroma"><code>n=5
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def rain_days(n) -&gt; 0.39968
</code></pre>

Given that it is raining today and that it rained yesterday, write a function to calculate the probability that it will rain on the nth day after today.

Rain in N Days

Suppose we have a binary classification model that classifies whether or not an applicant should be qualified to get a loan.

Because we are a financial company, we have to provide each rejected applicant with a reason why.

Given we don’t have access to the feature weights, how would we give each rejected applicant a reason why they got rejected?

In the context of a loan eligibility model, what does a partial dependence plot represent?

In the context of a loan eligibility model, what does a partial dependence plot represent?

Rejection Reason

How do we give each rejected applicant a reason why they got rejected?

Assume we have some credit model, which has a calibrated (up to some error) score of how credit-worthy any individual person is.

For example, if the model’s estimate is 83% then we can assume the actual score is between 81% and 85%.

If we take 83% as a score cutoff and deem everyone above that score as credit-worthy, are we over-estimating or underestimating the actual population’s credit score?

Question	Topic	Difficulty	Ask Chance
Rain in N Days	Probability	Hard	Very High
Rejection Reason	Machine Learning	Medium	Very High
Credit Score Estimation	Probability	Medium	Low

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