A Simple XGBoost Tutorial Using the Iris Dataset

#ICYMI A Simple XGBoost Tutorial Using the Iris Dataset  #MachineLearning

  • It is important to install it using Anaconda (in Anaconda’s directory), so that pip installs other libs there as well:

    Now, a very important step: install xgboost Python Package dependencies beforehand.

  • I install these ones from experience:

    I upgrade my python virtual environment to have no trouble with python versions:

    And finally I can install xgboost with pip (keep fingers crossed):

    This command installs the latest xgboost version, but if you want to use a previous one, just specify it with:

    Now test if everything is has gone well – type python in the terminal and try to import xgboost:

    If you see no errors – perfect.

  • First you load the dataset from sklearn, where X will be the data, y – the class labels:

    Then you split the data into train and test sets with 80-20% split:

    Next you need to create the Xgboost specific DMatrix data format from the numpy array.

  • Xgboost can work with numpy arrays directly, load data from svmlignt files and other formats.
  • Here is how to work with numpy arrays:

    If you want to use svmlight for less memory consumption, first dumpthe numpy array into svmlight format and then just pass the filename to DMatrix:

    Now for the Xgboost to work you need to set the parameters:

    Different datasets perform better with different parameters.


This is an overview of the XGBoost machine learning algorithm, which is fast and shows good results. This example uses multiclass prediction with the Iris dataset from Scikit-learn.

Continue reading “A Simple XGBoost Tutorial Using the Iris Dataset”

A Simple XGBoost Tutorial Using the Iris Dataset

A Simple XGBoost Tutorial Using the Iris Dataset  #MachineLearning

  • It is important to install it using Anaconda (in Anaconda’s directory), so that pip installs other libs there as well:

    Now, a very important step: install xgboost Python Package dependencies beforehand.

  • I install these ones from experience:

    I upgrade my python virtual environment to have no trouble with python versions:

    And finally I can install xgboost with pip (keep fingers crossed):

    This command installs the latest xgboost version, but if you want to use a previous one, just specify it with:

    Now test if everything is has gone well – type python in the terminal and try to import xgboost:

    If you see no errors – perfect.

  • First you load the dataset from sklearn, where X will be the data, y – the class labels:

    Then you split the data into train and test sets with 80-20% split:

    Next you need to create the Xgboost specific DMatrix data format from the numpy array.

  • Xgboost can work with numpy arrays directly, load data from svmlignt files and other formats.
  • Here is how to work with numpy arrays:

    If you want to use svmlight for less memory consumption, first dumpthe numpy array into svmlight format and then just pass the filename to DMatrix:

    Now for the Xgboost to work you need to set the parameters:

    Different datasets perform better with different parameters.


This is an overview of the XGBoost machine learning algorithm, which is fast and shows good results. This example uses multiclass prediction with the Iris dataset from Scikit-learn.

Continue reading “A Simple XGBoost Tutorial Using the Iris Dataset”

Google Deep Learning system diagnoses cancer better than a pathologist with unlimited time

Google Deep Learning system diagnoses cancer better than a pathologist with unlimited time

  • Google has been working on advanced image-recognition systems for several years through its GoogLeNet projects.
  • The project was, in part, aimed at the company’s autonomous car project, teaching self-driving cars to recognize everything from road layouts to stop signs.
  • The company has now applied GoogLeNet tech to cancer diagnosis, and reports that the system was already delivering good results straight out of the box, but says that tweaking the system has delivered stunning performance.
  • Pathologists have always faced a huge data problem in order to obtain an accurate diagnosis.

Google Deep Learning system diagnoses cancer better than a pathologist with unlimited time
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Google’s AI Learns How To Code Machine Learning Software — Bad News For Programmers?

Google's #AI Learns How To Code #MachineLearning Software

  • Google’s AI Learns How To Code Machine Learning Software
  • The Google Brain artificial intelligence research group has created a new machine learning system that can design machine-learning software.
  • In their experiment, the researchers challenged their software to create machine learning systems.
  • Short Bytes: A team of researchers at Google Brain AI research group has created an AI system that has designed its own machine learning software.
  • The software that came up with these designs used the power of 800 GPUs.

A team of researchers at Google Brain AI research group has created an AI system that has designed its own machine learning software. The software that came up with these designs used the power of 800 GPUs
Continue reading “Google’s AI Learns How To Code Machine Learning Software — Bad News For Programmers?”

GitHub

A #DeepLearning approach to colorizing images  #AI #MachineLearning

  • Use convert_images.py to create resized and gray images for training.
  • The training attempts to obtain the resized color image when given the resized gray image.
  • -n –normalize [y/n normalize training images]
  • /images/train/ -n n to start training on the small amount of sample images.
  • You can use this with some sample training images provided in images/train .

Colorful-Image-Colorization – A deep learning approach to colorizing images
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