[ ANNOUNCEMENT ]
I hope this note finds you well. Please excuse the brief interruption in our newsletter. Over past few weeks, we have been doing some A/B testing and mounting our Newsletter on our AI led coach TAO.ai. This newsletter and future versions would be using capability of TAO. As with any AI, it needs some training, so kindly excuse/report the rough edges.
– Team TAO/AnalyticsCLUB
[ COVER OF THE WEEK ]
Data security Source
[ LOCAL EVENTS & SESSIONS]
More WEB events? Click Here
[ AnalyticsWeek BYTES]
>> The What and Where of Big Data: A Data Definition Framework by bobehayes
>> The Cost Of Too Much Data by v1shal
>> Unraveling the Mystery of Big Data by v1shal
Wanna write? Click Here
[ NEWS BYTES]
How a Data Scientist’s Job ‘Play in Front’ than other BI and Analytic Roles – CIOReview Under Data Scientist
AI, Machine Learning to Reach $47 Billion by 2020 – Infosecurity Magazine Under Machine Learning
Software to âEncode the Mindsetâ of Lawyers – Lawfuel (blog) Under Prescriptive Analytics
More NEWS ? Click Here
[ FEATURED COURSE]
Lean Analytics Workshop – Alistair Croll and Ben Yoskovitz
[ FEATURED READ]
Storytelling with Data: A Data Visualization Guide for Business Professionals
[ TIPS & TRICKS OF THE WEEK]
Analytics Strategy that is Startup Compliant
With right tools, capturing data is easy but not being able to handle data could lead to chaos. One of the most reliable startup strategy for adopting data analytics is TUM or The Ultimate Metric. This is the metric that matters the most to your startup. Some advantages of TUM: It answers the most important business question, it cleans up your goals, it inspires innovation and helps you understand the entire quantified business.
[ DATA SCIENCE JOB Q&A]
Q:What is cross-validation? How to do it right?
A: It’s a model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set. Mainly used in settings where the goal is prediction and one wants to estimate how accurately a model will perform in practice. The goal of cross-validation is to define a data set to test the model in the training phase (i.e. validation data set) in order to limit problems like overfitting, and get an insight on how the model will generalize to an independent data set.
Examples: leave-one-out cross validation, K-fold cross validation
How to do it right?
the training and validation data sets have to be drawn from the same population
predicting stock prices: trained for a certain 5-year period, it’s unrealistic to treat the subsequent 5-year a draw from the same population
common mistake: for instance the step of choosing the kernel parameters of a SVM should be cross-validated as well
Bias-variance trade-off for k-fold cross validation:
Leave-one-out cross-validation: gives approximately unbiased estimates of the test error since each training set contains almost the entire data set (n?1n?1 observations).
But: we average the outputs of n fitted models, each of which is trained on an almost identical set of observations hence the outputs are highly correlated. Since the variance of a mean of quantities increases when correlation of these quantities increase, the test error estimate from a LOOCV has higher variance than the one obtained with k-fold cross validation
Typically, we choose k=5 or k=10, as these values have been shown empirically to yield test error estimates that suffer neither from excessively high bias nor high variance.
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