AI News, Machine Learning Showdown: Python vs R
Machine Learning Showdown: Python vs R
It’s going to revolutionize the world of finance, mobile advertising, or… some other world, but it’s definitely going to revolutionize something.
This article is meant to help developers choose between these two bitter rivals, in the context of machine learning (for a more general, feature-by-feature comparison you might want to check out this great infographic by DataCamp).
While both Python and R are completely manageable and used by many developers in both business and academia, Python lends itself more easily to developers who have experience with other programming languages.
Since you’re developing a machine learning app, we’re guessing you’re closer to the latter group –
While applications of R in the business world are definitely on a growth trajectory, Python is still a more full-fledged programming language and is used for many types of web and other applications, in addition to its data science applications.
Hence, assuming you would want to integrate your machine learning algorithms into some kind of interface that’s communicating with other code, written by other programmers, Python might be the better choice.
R can be used for rapid prototyping or to solve a specific problem, but Python will be easier to maintain and scale in the long run (especially considering its versioning and documentation are far more consistent).
Winner: Python Both languages have a breadth of external libraries that can be (relatively) easily used in a machine learning project, Python’s are a bit more mature.
but mastering scikit and similar libraries will provide you with a deeper and more complete toolset that you can feel safe using in your machine learning app.
especially when it comes to ‘one-off’ operations, prototyping and testing various hypotheses (versus creating reusable and extendible features).
With the necessary caveats that every application, use case and business scenario is different, Python is the more mature, fully-fledged and flexible option for machine learning –
Identify feature and response variable(s) and values must be numeric and numpy arrays
And the knowledge and mastery of a programming language is more important and effective than the features of that language compared to others.
The first dimension is the ability to explore, build proof of concepts or models at a fast pace, eventually having at hand enough tools to study what is going on (like statistical tests, graphics, measurement tools, etc).
This kind of activity is usually preferred by researchers and data scientists (I always wonder what that means, but I use this term for its loose definition).
The second dimension is the ability to extend, change, improve or even create tools, algorithms or models.
If you work for a company, than you depend a lot on the company's infrastructure, internal culture and your choices diminish significantly.
But after more than 17 years as a developer, I tend to prefer a strict contract and my knowledge, than the freedom to do whatever you might think of (like it happens with a lot of dynamic languages).
Python and R have developed robust ecosystems for data scientists
Machine learning and data analysis are two areas where open source has become almost the de facto license for innovative new tools.
Both the Python and R languages have developed robust ecosystems of open source tools and libraries that help data scientists of any skill level more easily perform analytical work.
The distinction between machine learning and data analysis is a bit fluid, but the main idea is that machine learning prioritizes predictive accuracy over model interpretability, while data analysis emphasizes interpretability and statistical inference.
That isn't to pigeonhole either language into one category—Python can be used effectively as a data analysis tool, and R has enough flexibility to do some good work in machine learning.
They are the core of data analysis in Python and any serious data analyst is likely using them raw, without higher-level packages on top, but scikit-learn pulls them together in a machine learning library with a lower barrier to entry.
Caret is another package that bolsters R's machine learning capabilities, in this case by offering a set of functions that increase the efficiency of predictive model creation.
Python is well known as a flexible language, so if you plan to move on to projects in other fields when your machine learning or data analysis project is done, it might be a good idea to stick with Python so you aren't required to learn a new language.
Python's flexibility makes it a great choice for production use because, when the data analysis tasks need to be integrated with Web applications, for example, you can continue to use Python instead of integrating with another language.
Labeling data, filling missing values, and filtering are all simple and intuitive in R, which emphasizes user-friendly data analysis, statistics, and graphical models.
Packages like statsmodels provide solid coverage for statistical models in Python, but the ecosystem of statistical model packages for R is much more robust.
As far as beginner programmers are concerned, R makes exploratory work easier than Python because statistical models can be written with just a few lines of code.
The resulting decrease in development speed comes from having to learn new ways to model data and make predictions with each new algorithm you use.
Python's reach makes it easy to recommend not only as a general purpose and machine learning language, but with its substantial R-like packages, as a data analysis tool, as well.
But if you're looking for a flexible, extensible, multi-purpose programming language that also excels in both machine learning and data analysis, Python is the clear choice.
Essentials of Machine Learning Algorithms (with Python and R Codes)
Note: This article was originally published on Aug 10, 2015 and updated on Sept 9th, 2017
Google’s self-driving cars and robots get a lot of press, but the company’s real future is in machine learning, the technology that enables computers to get smarter and more personal.
The idea behind creating this guide is to simplify the journey of aspiring data scientists and machine learning enthusiasts across the world.
How it works: This algorithm consist of a target / outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables).
Using these set of variables, we generate a function that map inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy on the training data.
This machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions.
These algorithms can be applied to almost any data problem: It is used to estimate real values (cost of houses, number of calls, total sales etc.) based on continuous variable(s).
In this equation: These coefficients a and b are derived based on minimizing the sum of squared difference of distance between data points and regression line.
And, Multiple Linear Regression(as the name suggests) is characterized by multiple (more than 1) independent variables. While finding best fit line, you can fit a polynomial or curvilinear regression.
It is a classification not a regression 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).
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).
source: statsexchange In the image above, you can see that population is classified into four different groups based on multiple attributes to identify ‘if they will play or not’.
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.
For 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)
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.
It is a classification technique based on Bayes’ theorem with an assumption of independence between predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.
Step 1: Convert the data set to frequency table Step 2: Create Likelihood table by finding the probabilities like Overcast probability = 0.29 and probability of playing is 0.64.
Yes) * P(Yes) / P (Sunny) Here we have P (Sunny |Yes) = 3/9 = 0.33, P(Sunny) = 5/14 = 0.36, P( Yes)= 9/14 = 0.64 Now, P (Yes |
However, it is more widely used in classification problems in the industry. K nearest neighbors is a simple algorithm that stores all available cases and classifies new cases by a majority vote of its k neighbors.
Its procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters).
We know that as the number of cluster increases, this value keeps on decreasing but if you plot the result you may see that the sum of squared distance decreases sharply up to some value of k, and then much more slowly after that.
grown as follows: For more details on this algorithm, comparing with decision tree and tuning model parameters, I would suggest you to read these articles: Python R Code
For example: E-commerce companies are capturing more details about customer like their demographics, web crawling history, what they like or dislike, purchase history, feedback and many others to give them personalized attention more than your nearest grocery shopkeeper.
How’d you identify highly significant variable(s) out 1000 or 2000? In such cases, dimensionality reduction algorithm helps us along with various other algorithms like Decision Tree, Random Forest, PCA, Factor Analysis, Identify based on correlation matrix, missing value ratio and others.
GBM is a boosting algorithm used when we deal with plenty of data to make a prediction with high prediction power. Boosting is actually an ensemble of learning algorithms which combines the prediction of several base estimators in order to improve robustness over a single estimator.
The XGBoost has an immensely high predictive power which makes it the best choice for accuracy in events as it possesses both linear model and the tree learning algorithm, making the algorithm almost 10x faster than existing gradient booster techniques.
It is designed to be distributed and efficient with the following advantages: The framework is a fast and high-performance gradient boosting one based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
Since the LightGBM is based on decision tree algorithms, it splits the tree leaf wise with the best fit whereas other boosting algorithms split the tree depth wise or level wise rather than leaf-wise.
So when growing on the same leaf in Light GBM, the leaf-wise algorithm can reduce more loss than the level-wise algorithm and hence results in much better accuracy which can rarely be achieved by any of the existing boosting algorithms.
Catboost can automatically deal with categorical variables without showing the type conversion error, which helps you to focus on tuning your model better rather than sorting out trivial errors.
My sole intention behind writing this article and providing the codes in R and Python is to get you started right away. If you are keen to master machine learning, start right away.
- On Thursday, January 17, 2019
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