Code Requirements You can install Conda for python which resolves all the dependencies for machine learning. Description Logistic regression is named for the function used at the core of the method, the logistic function. The logistic function, also called the sigmoid function was developed by statisticians to describe properties of population growth in ecology, rising quickly and maxing out at the carrying capacity of the environment. It’s an S-shaped curve that can take any real-valued number and map it into a value between 0 and 1, but never exactly at those limits. 1 / (1 + e^-value) For more information, see Some Notes Dataset- UCI-ML I have used only 2 features out of 32 to classify. Workign Example Execution To run the code, type run breast_cancer.m run breast_cancer.m Python Implementation Dataset- UCI-ML I have used 30 features to classify Instead of 0=benign and 1=malignant, I have used 1=benign and 2=malignant Acuracy ~ 92% Execution To run the code, type python B_Cancer.py python B_Cancer.
Code Requirements I am using Python 3.6.1 for this project. You need to install the following Python libraries.
1.NumPy (for documentation:http://www.numpy.org/) 2.Pandas (for documentation:http://pandas.pydata.org/) 3.Scikit-Learn (for documentation:http://scikit-learn.org/stable/) 4.Scikit-Learn (for documentation:http://scikit-learn.org/stable/) 5.Seaborn (for documentation:https://seaborn.pydata.org/)
I have been using Jupyter Notebook for this project. You can install Conda for python which resolves all the dependencies for machine learning. Description:
Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. Random decision forests correct for decision trees' habit of overfitting to their training set.
Logistic regression is named for the function used at the core of the method, the logistic function. The logistic function, also called the sigmoid function was developed by statisticians to describe properties of population growth in ecology, rising quickly and maxing out at the carrying capacity of the environment. It’s an S-shaped curve that can take any real-valued number and map it into a value between 0 and 1, but never exactly at those limits. 1 / (1 + e^-value)
For more information, see Some Notes Dataset- UCI-ML I have used only 2 features out of 32 to classify. Working Example
Execution To run the code, type run breast_cancer.m run breast_cancer.m Python Implementation Dataset- UCI-ML I have used 30 features to classify Instead of 0=benign and 1=malignant, I have used 1=benign and 2=malignant Acuracy ~ 92%
Execution To run the code, type python Breast Cancer Classfication Deep Neural Network.ipynb python Breast Cancer Random Forest.ipynb