How to Calibrate Undersampled Model ScoresImbalanced data problems in binary prediction models and a simple but effective way to take care…
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Can you Solve TED’s Frog Riddle? Can TED?
Critics argue that it’s not. For those of you who want to see the problem laid out in detail, you…
Continue ReadingProbability — Fundamentals of Machine Learning (Part 1)
By plugging this into the chain rule, we find that in this scenario we get P(x, y) = P(x|y) ⋅…
Continue ReadingLearning NLP Language Models with Real Data
There are far to many possible sentences in this method that would need to be calculated and we would like…
Continue ReadingStatistics is the Grammar of Data Science — Part 2
To visualise the probability, we plot the dataset as a curve. The area under the curve between two points corresponds…
Continue ReadingAre you mixing up odds with probability?
And in high school they tend to teach us about probabilities, not odds. A probability is defined as the number…
Continue ReadingMarkov Chain Monte Carlo in Python
Markov Chain Monte Carlo in PythonWill KoehrsenBlockedUnblockFollowFollowingFeb 9, 2018A Complete Real-World ImplementationThe past few months, I encountered one term again and…
Continue ReadingMarketing Analytics through Markov Chain
Marketing Analytics through Markov ChainRidhima KumarBlockedUnblockFollowFollowingJan 6Image Source : http://setosa. io/ev/markov-chains/Imagine you are a company selling a fast-moving consumer good in the…
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Hyper-parameter OptimizationJon-Cody SokollBlockedUnblockFollowFollowingJan 3Photo by Paul Green on UnsplashIf you were to count all the possible classification algorithms and their parameters…
Continue ReadingProbability theory and the optimal dating strategy for 2018
It’s 1/N. And as n gets larger the larger timeframe we consider, this probability will tend to zero. Alright, you…
Continue ReadingDice, Polls & Dirichlet Multinomials
Even with increasingly better computational tools, such as MCMC, models based on conjugate distributions are advantageous.Beta-BinomialOne of the better known…
Continue ReadingUnfolding Naive Bayes from Scratch: Part 2
When doing the calculations of probability of the given test sentence in the above section, we did nothing but implement…
Continue ReadingProbability Part 2: Conditional Probability
By thinking of conditioning as a restriction on the size of the event space, we can measure the conditional probability…
Continue ReadingBayes’ Theorem: The Holy Grail of Data Science
1 Statistical resultsThe figure tells us that we have picked…… 148 times a blueberry from the bowl X: n(s=X, y=B)=148……
Continue ReadingMonty Hall’s paradox — solve it by simulation!
D in our case is when the host choosing door B and there is no price behind it.Let’s create a…
Continue ReadingJourney to Understand Bayes’ Theorem Visually
There is also a possibility for another event B to occur after A and the odds of that are denoted…
Continue ReadingUsing Markov Chain Monte Carlo method for project estimation
In particular, we are interested in finding the number of story points we can complete in one iteration with 95%…
Continue ReadingNaive Bayes classification from Scratch in Python
All together posterior probability in terms of the joint probability distribution (neglecting denominator P(x)) is written as:Now to calculate each…
Continue ReadingBayesian Convolutional Neural Networks with Bayes by Backprop
This results in the subsequent equation for convolutional layer activations b:where ϵj ∼ N(0, 1), Ai is the receptive field,…
Continue ReadingThe naive Bayes classifier
In this case, what is the probability Y belongs to a distinct class k given an observation x?πₖ is the…
Continue ReadingProbability and Statistics explained in the context of deep learning
Probability and Statistics explained in the context of deep learningPhoto by Josh Appel on UnsplashThis article is intended for beginners in deep…
Continue ReadingReceiver Operating Characteristic Curves Demystified (in Python)
The model performance is determined by looking at the area under the ROC curve (or AUC)..To create this, probability distribution,…
Continue ReadingProbability & Statistics for Data Science (Series)
I would like to mention that my focus in these posts would be to give intuition on every topic and…
Continue ReadingEntropy is a measure of uncertainty
Given certain assumptions (and foreshadowing an important result mentioned below), entropy is the measure of uncertainty.By the way, when I…
Continue ReadingHow I Learned to Stop Worrying and Love Uncertainty
Such a likelihood is impossible to measure for frequentist statistics, so when the question (central in science) is posed “How…
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