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Description : BY EXAMPLE... let’s say your friend gives you a puzzle to solve. There are only 2 outcome scenarios – either you solve it or you don’t. Now imagine, that you are being given wide range of puzzles / quizzes in an attempt to understand which subjects you are good at. The outcome to this study would be something like this – if you are given a trigonometry based tenth grade problem, you are 70% likely to solve it. On the other hand, if it is grade fifth history question, the probability of getting an answer is only 30%. This is what Logistic Regression provides you. It is a classification algorithm. It is used to estimate discrete values ( Binary values like 0/1, yes/no, true/false ) based on given set of independent variable(s). In simple words, it predicts the probability of occurrence of an event by fitting data to a logit function. Hence, it is also known as logit regression. Since, it predicts the probability, its output values lies between 0 and 1 (as expected). There are many different steps that could be tried in order to improve the model: including interaction terms, removing features regularization techniques, using a non-linear model. Algorithm : Coming to the math, the log odds of the outcome is modeled as a linear combination of the predictor variables. odds= p/ (1-p) = probability of event occurrence / probability of not event occurrence ln(odds) = ln(p/(1-p)) logit(p) = ln(p/(1-p)) = b0+b1X1+b2X2+b3X3....+bkXk Above, p is the probability of presence of the characteristic of interest. It chooses parameters that maximize the likelihood of observing the sample values rather than that minimize the sum of squared errors (like in ordinary regression).

Description : The best way to understand how decision tree works, is to play Jezzball – a classic game from Microsoft (image below). Essentially, you have a room with moving walls and you need to create walls such that maximum area gets cleared off with out the balls. So, every time you split the room with a wall, you are trying to create 2 different populations with in the same room. Decision trees work in very similar fashion by dividing a population in as different groups as possible . Surprisingly, it works for both categorical and continuous dependent variables . In this algorithm, we split the population into two or more homogeneous sets. This is done based on most significant attributes/ independent variables to make as distinct groups as possible. Algorithm : By example, to split the population into different heterogeneous groups, it uses various techniques like Gini, Information Gain, Chi-square, entropy.

Description : By example, if we only had two features like Height and Hair length of an individual, we’d first plot these two variables in two dimensional space where each point has two co-ordinates (these co-ordinates are known as Support Vectors).   w, we will find some line that splits the data between the two differently classified groups of data. This will be the line such that the distances from the closest point in each of the two groups will be farthest away.  In the example shown above, the line which splits the data into two differently classified groups is the black line, since the two closest points are the farthest apart from the line. This line is our classifier. Then, depending on where the testing data lands on either side of the line, that’s what class we can classify the new data as. It is a classification method.  Algorithm : In this algorithm, we plot each data item as a point in n-dimensional space (where n is number of features you have) with the value of each feature being the value of a particular coordinate.

Danielle Costa Nakano Data Strategy Digital Transformation Omni-channel personalization Product Strategy Product Management Data Products Data Science Artificial Intelligence Predictive Analytics Machine Learning R&D 20+ years of experience building businesses - always looking to turn challenges to into opportunities - coaching high-functioning teams - business value, data-driven strategy - measurable outcomes Highlights - data strategy, product management, digital transformation - omni-channel personalization, data monetization - automation, business intelligence, predictive analytics, artificial intelligence next generation data & technology GRASSROOTS ANALYTICS iconic, global brand & nonprofit NATIONAL GEOGRAPHIC SOCIETY political technology market leader NGP VAN social good private equity software BONTERRA TECH nonprofit fundraising & advocacy technology EVERYACTION Industry Associations DATA PRODUCTS LEADERSHIP COMMUNITY 2023 - 2025 INTERNATIONAL INSTITUE OF BUSINESS ANALYSTS 2009 - 2016