OBSERVATION


Interpreting Decision Trees and Random Forests

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We will try to predict the number of rings based on variables such as shell weight, length, diameter, etc. We can see from the below plot that this specific abalone's weight and length values negatively impact its predicted number of rings. If we plot the value of shell weight compared to its contribution, we gain the insight that increasing shell weight results an increase in contribution. Lower shucked weight values have no contribution, higher shucked weight values have negative contribution, and in between, the contribution is positive.


Top Data Mining Algorithms Identified by IEEE & Related Python Resources

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C4.5 builds decision trees from a set of training data in the same way as ID3, using the concept of information entropy. Support vector machines(SVMs) are supervised learning models with learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. Given a set of marked training examples, an SVM training algorithm builds a model that assigns new examples into one of marked categories. It is a link analysis algorithm and it assigns a numerical weighting called page rank to each element of a hyperlinked set of documents, with the purpose of "measuring" its relative importance within the set.


Machine Learning as a Service – MLaaS

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To help fill the information gap on feature engineering, MLaaS hands-on can help and support beginning-to-intermediate data scientists how to work with this widely practiced phenomena. Explaining or gaining common practices and mathematical principles to help engineer features for new data and tasks. MLaaS these days provides full automation of essential, yet time-consuming activities in predictive model construction, such as fast variable selection, variable interaction modeling, and variable transformations or best model selection. Conclusion – At end and at heart we all now the dirty secret no matter how good the algorithm is, no matter how good I as data scientist, no model can perform magic if direction, intension, time and goal is not set.


Outlier Detection with Parametric and Non-Parametric methods

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Additionally, you could do a univariate analysis by studying a single variable at a time or multivariate analysis where you would study more than one variable at the same time to identify outliers. The x-axis, in the above plot, represents the Revenues and the y-axis, probability density of the observed Revenue value. The density curve for the actual data is shaded in'pink', the normal distribution is shaded in'green' and log normal distribution is shaded in'blue'. The probability density for the actual distribution is calculated from the observed data, whereas for both normal and log-normal distribution is computed based on the observed mean and standard deviation of the Revenues.


Python for brain mining: (neuro)science with state of the art machine…

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Python for brain mining:(neuro)science with state of the art machine learningand data visualization Ga l Varoquaux e 1. Data-driven science "Brain mining" 2. Data mining in Python Mayavi, scikit-learn, joblib 1 Brain mining Learning models of brain functionGa l Varoquaux e 2 1 Imaging neuroscience Brain Models of images function Cognitive tasksGa l Varoquaux e 3 1 Imaging neuroscience Brain Models of images function Data-driven science i Cognitive HΨ Ψ tasks tGa l Varoquaux e 3 1 Brain functional data Rich data 50 000 voxels per frame Complex underlying dynamics Few observations 100 Drawing scientific conclusions? Opt for simplicity Prefer algorithms to framework Code quality: consistency and testingGa l Varoquaux e 13 2 scikit-learn: statistical learningAPI Inputs are numpy arrays Learn a model from the data: estimator.fit(X Low barrier of entry Friendly and very skilled mailing list Credit to peopleGa l Varoquaux e 16 2 joblib: Python functions on steroids We keep recomputing the same things Nested loops with overlapping sub-problems Varying parameters I/O Standard solution: pipelines Challenges Dependencies modeling Parameter trackingGa l Varoquaux e 17 2 joblib: Python functions on steroids Philosophy Simple don't change your code Minimal no dependencies Performant big data Robust never fail joblib's solution lazy recomputation: Take an MD5 hash of function arguments, Store outputs to diskGa l Varoquaux e 18 2 joblib Lazy recomputing from j o b l i b import Memory mem Memory ( c a c h e d i r '/ tmp / joblib ') import numpy a s np a np .


Machine Learning as a Service – MLaaS

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To help fill the information gap on feature engineering, MLaaS hands-on can help and support beginning-to-intermediate data scientists how to work with this widely practiced phenomena. Explaining or gaining common practices and mathematical principles to help engineer features for new data and tasks. MLaaS these days provides full automation of essential, yet time-consuming activities in predictive model construction, such as fast variable selection, variable interaction modeling, and variable transformations or best model selection. Conclusion – At end and at heart we all now the dirty secret no matter how good the algorithm is, no matter how good I as data scientist, no model can perform magic if direction, intension, time and goal is not set.


Machine Learning Fundamentals: Predicting Airbnb Prices

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Each row in the data set is a specific listing that's available for renting on Airbnb in the Washington, D.C. area To make the data set less cumbersome to work with, we've removed many of the columns in the original data set and renamed the file to dc_airbnb.csv. The K-nearest neighbors (knn) algorithm is very similar to the three step process we outlined earlier to compare our listing to similar listings and take the average price. We've now made our first prediction -- our simple knn model told us that when we're using just the accommodates feature to make predictions of our listing that accommodates three people, we should list our apartment for $88.00. We can instead take the mean of the squared error values, which is called the root mean squared error (RMSE).


Time Series Forecasting with the Long Short-Term Memory Network in Python - Machine Learning Mastery

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A line plot of the test dataset (blue) compared to the predicted values (orange) is also created showing the persistence model forecast in context. It takes a NumPy array of the raw time series data and a lag or number of shifted series to create and use as inputs. The trend can be removed from the observations, then added back to forecasts later to return the prediction to the original scale and calculate a comparable error score. Running the example first prints the first 5 rows of the loaded data, then the first 5 rows of the scaled data, then the first 5 rows with the scale transform inverted, matching the original data.


Machine Learning as a Service – MLaaS

#artificialintelligence

To help fill the information gap on feature engineering, MLaaS hands-on can help and support beginning-to-intermediate data scientists how to work with this widely practiced phenomena. Explaining or gaining common practices and mathematical principles to help engineer features for new data and tasks. MLaaS these days provides full automation of essential, yet time-consuming activities in predictive model construction, such as fast variable selection, variable interaction modeling, and variable transformations or best model selection. Conclusion – At end and at heart we all now the dirty secret no matter how good the algorithm is, no matter how good I as data scientist, no model can perform magic if direction, intension, time and goal is not set.


Boosting the accuracy of your Machine Learning models

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An easy way of estimating the test error of a bagged model, without the need for cross-validation is Out-of-Bag Error Estimation. The observations not used to fit a given bagged tree are referred to as the out-of-bag (OOB) observations. We average those predicted responses, or take a majority vote, depending on if the response is quantitative or qualitative. This is an acceptable test error rate because the predictions are based on only the trees that were not fit using that observation.