AI News, Data Science Rosetta Stone: Classification in Python, R, MATLAB, SAS, Julia

Data Science Rosetta Stone: Classification in Python, R, MATLAB, SAS, Julia

The discovery of the Rosetta Stone in 1799 allowed scholars to finally decipher ancient Egyptian hieroglyphics.

The actual content of the Rosetta stone is a very mundane government decree issued by the Egyptian government around 196 BC.

The importance of the Rosetta stone is that this mundane decree was written in Ancient Egyptian using both hieroglyphic script and Demotic script, as well as Ancient Greek.

This article attempts a “Rosetta stone” of data science by showing a simple classification in the following languages: We will begin with a simple data science classification problem.

Classification is where you would like the model to learn to identify a non-numeric outcome for input data.

In this case, we would like to input information about passengers on the RMS Titanic and get a prediction on if that passenger might survive.

There are more complex models, such as random forests, gradient boosted machines, or deep neural networks.

However, because the purpose of this article is to highlight the languages, a relatively simple model was chosen.

Python is a general purpose programming language that is widely used both by data scientists and software developers alike.

The first section simply imports the needed libraries and defines a convenience function that will encode dummy variables.

According to KDDNuggets, R is the most popular programming language for data science – but it is pretty close.

Additionally, there is no need to encode sex or embarked, because R loaded these values as factors (categoricals) and they are automatically encoded.

However, the macro language functions as macros and effectively repeats PROC and DATA segments of the source file.

The OUT parameter specifies that the CSV file will be stored in a temporary binary file named train.

The first step is to add a column, named selected to the train data set that specifies if it is part of the ultimate training set or not.

This is done by using PROC SURVEYSELECT, a total of 70% of the data will have a selected variable with a value of 1.

The previous train data set (which held all rows) is replaced by a new train data set that contains only the training data.

Once the train data set has been properly labeled, it is split into two data sets that are named train and validate.

Now that we have a train and validation data set we are ready to fit the Logistic regression.

The model parameters themselves are written to a binary file called model.

The descending flag specifies that the model will predict values of 1 (survived), as opposed to 0 (perished).

This will create a data set named pred that contains the validate data set augmented with predictions.

Because we are predicting a binary outcome, two additional columns are added to the data set: P_1 specifies the probability that the person survived and P_2 specifies the probability that the person perished.

Next a prediction data set is created where any Survived probability is greater than or equal to 0.5 is assumed to have survived is created.

Additionally, a new column named pred_survived is created that holds a value of 1 if the probability of survival was greater or equal than 0.5.

Julia is a relatively new programming language that the data science community is showing increased support for.

Julia can use advanced linear algebra packages to perform matrix operations with great performance;

prefix/suffix, such as .~ means that the not(~) is applied to each member of the vector, not the whole vector.

This rounding means that probabilities of survival of 0.5 and higher indicate survived (1);

A New Multi-Turn, Multi-Domain, Task-Oriented Dialogue Dataset

Task-oriented dialogue focuses on conversational agents that participate in user-initiated dialogues on domain-specific topics.

In an effort to help alleviate this problem, we release a corpus of 3,031 multi-turn dialogues in three distinct domains appropriate for an in-car assistant: calendar scheduling, weather information retrieval, and point-of-interest navigation.

Tasks were randomly specified by selecting values (5pm, Saturday, San Francisco, etc.) for three to five slots (time, date, location, etc.) that depended on the domain type.

In the Car Assistant mode, users were presented with the dialogue history exchanged up to that point in the running dialogue and a private knowledge base known only to the Car Assistant with information that could be useful for satisfying the Driver query.

Examples of knowledge bases could include a calendar of event information, a collection of weekly forecasts for nearby cities, or a collection of nearby points-of-interest with relevant information.

The private knowledge bases used were generated by uniformly selecting a value for a given attribute type, where each attribute type had a variable number of candidate values.

While specifying the attribute types and values in each task presented to the Driver allowed us to ground the subject of each dialogue with our desired entities, it would occasionally result in more mechanical discourse exchanges.

To encourage more naturalistic, unbiased utterances, we had users record themselves saying commands in response to underspecified visual depictions of an action a car assistant could perform.

Source: Original Owner and Donor: Mohammed Waleed Kadous School of Computer Science of Engineering University of New South Wales Sydney NSW 2052 Australia waleed '@' Data Set Information: The source of the data is the raw measurements from a Nintendo PowerGlove.

Position information is calculated on the basis of ultrasound emissions from emitters the glove to a 3-microphone 'L-Bar' that sits atop a monitor.

This allows the calculation of 4 pieces of information: x (left/right), y (up/down), z (backward/forward), and roll (is the palm pointing up or down?).

In particular, 1 unit in the z direction is not of similar distance to 1 unit in the x or y directions.

These x, y, z positions are relative to a calibration point which is when the palm is resting on the seated signer's thigh.

The data was collected from five signers: Signer -- Description -- Sessions -- Total samples/sign Adam -- Sign linguist - PhD completed in area.

-- 2 -- 8 Andrew -- Natural signer - signing since youth -- 3 -- 8 John -- Professional Auslan interpreter -- 5 -- 18 Stephen -- Professional Auslan interpreter -- 4 -- 16 Waleed -- The researcher.

This space should not really be treated as linear, although it is safe to treat it as monotonically increasing.

- Description: roll with 0 meaning 'palm down', rotating clcokwise through to a maximum of 1 (not included), which is also 'palm down'.

yaw: - Has a value of -1, indicating that it is not available for this data.

little: - In this case, it is a copy of ring bend.

gs1: - glove state 1 Should be ignored.

gs2: - glove state 2 should be ignored.

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