AI News, Open Machine Learning Course. Topic 4. Linear Classification and Regression

Open Machine Learning Course. Topic 4. Linear Classification and Regression

The training dataset contains the following features: User sessions are chosen in such a way that they are not longer than half an hour and/or contain more than ten websites;

In case of enormously large sets, it may turn out that it is impossible to transform both datasets simultaneously (and sometimes you have to split your transformations into several stages, separately for the train/test dataset).

According to our hypothesis (Alice has favorite websites), we need to transform this dataframe so that each website has the corresponding feature (column) which value is equal to the number of visits on this website within the session.

It’s easy to calculate the required amount of memory, roughly: 336K * 48K * 8 bytes = 16M * 8 bytes = 128 GB Obviously, mere mortals don’t have such volumes of memory (strictly speaking, Python may allow you to create such a matrix, but it would not be easy to do anything with it).

Such a matrix, where most elements are zeros, is called sparse, and the ratio between the number of zero elements and the total number of elements is called the sparseness of the matrix.

To work with such matrices, you can use scipy.sparse library, check the documentation to understand what possible types of sparse matrices are, how to work with them and in which cases their usage is most effective.

we want to transform the previous table into the following form: To do this, use the constructor: csr_matrix ((data, indices, indptr)) and create a frequency table (see examples, code and comments on the links above to see how it works).

Another benefit of using sparse matrices is that there are special implementations of both matrix operations and machine learning algorithms for them, which sometimes allows to significantly accelerate operations due to the data structure peculiarities.

To make a prediction on the test set, we need to train the model again on the entire training dataset (until this moment, our model used only part of the data for training), which will increase its generalizing ability: If you follow these steps and upload the answer to the competition page, then you should get the quality of ROC AUC = 0.91707 on the public leaderboard.

Data type mapping between R and Spark

data files, tables in Hive, external databases, or existing local R data frames.

All of the examples on this page use sample data included in R or the Spark distribution and can be run using the ./bin/sparkR shell.

The simplest way to create a data frame is to convert a local R data frame into a SparkDataFrame.

You can check the Spark SQL programming guide for more specific options that are available for the built-in data sources.

This method takes in the path for the file to load and the type of data source, and the currently active SparkSession will be used automatically. SparkR

supports reading JSON, CSV and Parquet files natively, and through packages available from sources like Third Party Projects, you can find data source connectors for popular file formats like Avro.

we include some basic examples and a complete list can be found in the API docs: SparkR data frames support a number of commonly used functions to aggregate data after grouping.

For example we can compute a histogram of the waiting time in the faithful dataset as shown below In addition to standard aggregations, SparkR supports OLAP cube operators cube: and rollup: SparkR also provides a number of functions that can directly applied to columns for data processing and during aggregation.

Note that dapplyCollect can fail if the output of UDF run on all the partition cannot be pulled to the driver and fit in driver memory.

The function is to be applied to each group of the SparkDataFrame and should have only two parameters: grouping key and R data.frame corresponding to that

SparkR supports the following machine learning algorithms currently: Under the hood, SparkR uses MLlib to train the model.

can call summary to print a summary of the fitted model, predict to make predictions on new data, and to save/load fitted models. SparkR

supports a subset of the available R formula operators for model fitting, including ‘~’, ‘.’, ‘:’, ‘+’, and ‘-‘.

The following functions are masked by the SparkR package: Since part of SparkR is modeled on the dplyr package, certain functions in SparkR share the same names with those in dplyr.

Depending on the load order of the two packages, some functions from the package loaded first are masked by those in the package loaded after.

Quick and Dirty Data Analysis with Pandas

Before you can select and prepare your data for modeling, you need to understand what you’ve got to start with.

In this post you will discover some quick and dirty recipes for Pandas to improve the understanding of your data in terms of it’s structure, distribution and relationships.

These problems also apply when you are learning applied machine learning either with standard machine learning data sets, consulting or working on competition data sets.

The strength of Pandas seems to be in the data manipulation side, but it comes with very handy and easy to use tools for data analysis, providing wrappers around standard statistical methods in statsmodels and graphing methods in matplotlib.

The UIC Machine Learning repository provides a vast array of different standard machine learning datasets you can use to study and practice applied machine learning.

The dataset describes the onset or lack of onset of diabetes in female Pima Indians using details from their medical records. (update: download from here).

Such as the average number of pregnancies is 3.8, the minimum age is 21 and some people have a body mass index of 0, which is impossible and a sign that some of the attribute values should be marked as missing.

Each time you review the data a different way, you open yourself up to noticing different aspects and potentially achieving different insights into the problem.

You can generate a matrix of histograms for each attribute and one matrix of histograms for each class value, as follows: The data is grouped by the class attribute (two groups) then a matrix of histograms is created for the attributes is in each group.

You can better contrast the attribute values for each class on the same plot This groups the data by class by only plots the histogram of plas showing the class value of 0 in red and the class value of 1 in blue.

We started out looking at quick and dirty one-liners for loading our data in CSV format and describing it using summary statistics.

We looked at the distribution of the data in box and whisker plots and histograms, then we looked at the distribution of attributes compared to the class attribute and finally at the relationships between attributes in pair-wise scatter plots.

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