Category Archives: AI

SVM RBF Kernel Parameters with Code Examples

SVM RBF Kernel Parameters - Gamma and C values

In this post, you will learn about SVM RBF (Radial Basis Function) kernel hyperparameters with the python code example.  The following are the two hyperparameters which you need to know while training a machine learning model with SVM and RBF kernel: Gamma  C (also called regularization parameter) Knowing the concepts on SVM parameters such as Gamma and C used with RBF kernel will enable you to select the appropriate values of Gamma and C and train the most optimal model using the SVM algorithm.  Let’s understand why we should use kernel functions such as RBF. Why use RBF Kernel? When the data set is linearly inseparable or in other words, the …

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Machine Learning – SVM Kernel Trick Example

In this post, you will learn about what are kernel methods, kernel trick, and kernel functions when referred with a Support Vector Machine (SVM) algorithm. A good understanding of kernel functions in relation to the SVM machine learning (ML) algorithm will help you build/train the most optimal ML model by using the appropriate kernel functions. There are out-of-box kernel functions such as some of the following which can be applied for training models using the SVM algorithm: Polynomial kernel Gaussian kernel Radial basis function (RBF) kernel Sigmoid kernel The following topics will be covered: Background – Why Kernel concept? What is a kernel method? What is the kernel trick? What are …

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Sklearn SVM Classifier using LibSVM – Code Example

In this post, you learn about Sklearn LibSVM implementation used for training an SVM classifier, with code example.  Here is a great guide for learning SVM classification, especially, for beginners in the field of data science/machine learning. LIBSVM is a library for Support Vector Machines (SVM) which provides an implementation for the following: C-SVC (Support Vector Classification) nu-SVC epsilon-SVR (Support Vector Regression) nu-SVR Distribution estimation (one-class SVM) In this post, you will see code examples in relation to C-SVC, and nu-SVC LIBSVM implementations. I will follow up with code examples for SVR and distribution estimation in future posts. Here are the links to their SKLearn pages for C-SVC and nu-SVC …

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SVM – Understanding C Value with Code Examples

SVM Soft Margin CLassifier and C Value

In this post, we will understand the importance of C value on the SVM soft margin classifier overall accuracy using code samples. In the previous post titled as SVM as Soft Margin Classifier and C Value, the concepts around SVM soft margin classifier and the importance of C value was explained. If you are not sure about the concepts, I would recommend reading earlier article. Lets take a look at the code used for building SVM soft margin classifier with C value. The code example uses the SKLearn IRIS dataset  In the above code example, take a note of the value of C = 0.01. The model accuracy came out to …

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SVM as Soft Margin Classifier and C Value

Soft margin classifier SVM and importance of C value

In this post, you will learn about SVM (Support Vector Machine) as Soft Margin Classifier and the importance of Value of C. In the previous post, we learned about SVM as maximum margin classifier. What & Why of SVM as Soft Margin Classifier? Before getting into understanding what is Soft Margin Classifier version of SVM algorithm, lets understand why we need it when we had a maximum margin classifier. Maximum margin classifier works well with linearly separable data such as the following: When maximum margin classifier is trained on the above data set with maximum distance (margin) between the closest points (support vectors), we can get a hyperplane which can separate the data in a clear …

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Top 5 Data Analytics Methodologies

analytics methods

Here is a list of top 5 data analytics methodologies which can be used to solve different business problems and in a way create business value for any organization: Optimization: Simply speaking, an optimization problem consists of maximizing or minimizing a real function by systematically choosing input values (also termed as decision variables) from within an allowed set and computing the value of the function. An optimization problem consists of three things: A. Objective function B. Decision variables C. Constraint functions (this is optional) Linear / Non-linear programming with constrained / unconstrained optimization Linear programming with constrained optimization Objective function and one or more constraint functions are linear with decision variables as continuous variables Linear programming with unconstrained optimization Objective function …

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Machine Learning Use Cases in Procurement

procurement machine learning use cases

This post represents some of the important machine learning use cases in the procurement domain. These use cases can also be categorised as predictive analytics use cases for procurement. The list is not aimed to be exhaustive. However, some of the most important ones are listed. In case, you would like to add one or more use cases which I might have missed, pls feel free to suggest. The following are five key business function areas / department in procurement department.  Demand management Category management Supplier management Sourcing management Contract management In all of the above function areas, there can be multiple use cases which can take advantage of machine …

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Contract Management Use Cases for Machine Learning

contract management use cases machine learning

This post briefly represent the contract management use cases which could be solved using machine learning / data science. These use cases can also be termed as predictive analytics use cases. This can be useful for procurement business functions in any manufacturing companies which require to procure raw materials from different suppliers across different geographic locations. The following are some of the examples of industry where these use cases and related machine learning techniques can be useful. Pharmaceutical Airlines Food Transport Key Analytics Questions One must understand the business value which could be created using predictive analytics use cases listed later in this post. One must remember that one must start with questions …

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Classification Model with SVM Classifier – Python Example

In this post, you will get an access to Python code example for building a machine learning classification model using SVM (Support Vector Machine) classifier algorithm. We will work with Python Sklearn package for building the model. The following steps will be covered for training the model using SVM: Load the data Create training and test split Perform feature scaling Instantiate an SVC classifier Fit the model Measure the model performance First and foremost we will load appropriate Sklearn modules and classes. Lets get started with loading the data set and creating the training and test split from the data set. Pay attention to the stratification aspect used when creating the training and test split. The train_test_split class of sklearn.model_selection …

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Why Deep Learning is called Deep Learning?

What is deep learning

In this post, you will learn why deep learning is called as deep learning. You may recall that deep learning is a subfield of machine learning. One of the key difference between deep learning and machine learning is in the manner the representations / features of data is learnt. In machine learning, the representations of data need to be hand-crafted by the data scientists. In deep learning, the representations of data is learnt automatically as part of learning process. As a matter of fact, in deep learning, layered representations of data is learnt. The layered representations of data are learnt via models called as neural networks. The diagram below represents …

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Difference – Artificial Intelligence & Machine Learning

Artificial intelligence vs machine learning

In this post, you learn the difference between artificial intelligence & machine learning. Artificial intelligence represents a set of computer programs that imitate human intelligence. The diagram below represents the key difference between AI and Machine Learning. Basically, machine learning is a part of AI landscape. One can do AI without doing machine learning or deep learning. Thus, an organization can claim that they have AI-based systems without having machine learning or deep learning based systems.  All machine learning or deep learning based systems can be termed as AI systems. But, all AI systems may not be termed as machine learning systems. The following are key building blocks of an …

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Machine Learning Models Evaluation Infographics

model evaluation techniques

In this post, you will get an access to a self-explanatory infographics / diagram representing different aspects / techniques which need to be considered while doing machine learning model evaluation. Here is the infographics:   In the above diagram, you will notice that the following needs to be considered once the model is trained. This is required to be done to select one model out of many models which get trained. Basic parameters: The following need to be considered for evaluating the model: Bias & variance Overfitting & underfitting Holdout method Confidence intervals Resampling methods: The following techniques need to be adopted for evaluating models: Repeated holdout Empirical confidence intervals Cross-validation: Cross …

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Infographics for Model & Algorithm Selection & Evaluation

model evaluation model selection algorithm comparison

This is a short post created for quick reference on techniques which could be used for model evaluation & selection and model and algorithm comparision. This would be very helpful for those aspiring data scientists beginning to learn machine learning or those with advanced data science skills as well. The image has been taken from this blog, Comparing the performance of machine learning models and algorithms using statistical tests and nested cross-validation authored by Dr. Sebastian Raschka The above diagram provides prescription for what needs to be done in each of the following areas with small and large dataset. Very helpful, indeed. Model evaluation Model selection Model and algorithm comparison …

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Feature Scaling & Stratification for Model Performance (Python)

In this post, you will learn about how to improve machine learning models performance using techniques such as feature scaling and stratification. The following topics are covered in this post. The concepts have been explained using Python code samples. What is feature scaling and why one needs to do it? What is stratification? Training Perceptron model without feature scaling and stratification Training Perceptron model with feature scaling Training Perceptron model with feature scaling and stratification What is Feature Scaling and Why is it needed? Feature scaling is a technique of standardizing the features present in the data in a fixed range. This is done when data consists of features of varying …

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Python – Improve Model Performance using Feature Scaling

In this post you will learn about a simple technique namely feature scaling using which you could improve machine learning models. The models will be trained using Perceptron (single-layer neural network) classifier. First and foremost, lets quickly understand what is feature scaling and why one needs it? What is Feature Scaling and Why does one need it? Feature Scaling is a technique to standardize the independent features present in the data in a fixed range. This is performed when the dataset contains features that are highly varying in magnitudes, units, and ranges. The following are a different kind of scaling: Min-max scaling: The input value is scaled to the range of …

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Python DataFrame – Assign New Labels to Columns

Python Dataframe Columns - Labels assigned new value

In this post, you will get a code sample related to how to assign new labels to columns in python programming while training machine learning models.  This is going to be very helpful when working with classification machine learning problem. Many a time the labels for response or dependent variable are in text format and all one wants is to assign a number such as 0, 1, 2 etc instead of text labels. Beginner-level data scientists will find this code very handy. We will look at the code for the dataset as represented in the diagram below: In the above code, you will see that class labels are named as very_low, Low, High, Middle …

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