Author Archives: Ajitesh Kumar
Feature Extraction using PCA – Python Example
In this post, you will learn about how to use principal component analysis (PCA) for extracting important features (also termed as feature extraction technique) from a list of given features. As a machine learning / data scientist, it is very important to learn the PCA technique for feature extraction as it helps you visualize the data in the lights of importance of explained variance of data set. The following topics get covered in this post: What is principal component analysis? PCA algorithm for feature extraction PCA Python implementation step-by-step PCA Python Sklearn example What is Principal Component Analysis? Principal component analysis (PCA) is an unsupervised linear transformation technique which is primarily used …
Eigenvalues & Eigenvectors with Python Examples
In this post, you will learn about how to calculate Eigenvalues and Eigenvectors using Python code examples. Before getting ahead and learning the code examples, you may want to check out this post on when & why to use Eigenvalues and Eigenvectors. As a machine learning Engineer / Data Scientist, you must get a good understanding of Eigenvalues / Eigenvectors concepts as it proves to be very useful in feature extraction techniques such as principal components analysis. Python Numpy package is used for illustration purpose. The following topics are covered in this post: Creating Eigenvectors / Eigenvalues using Numpy Linalg module Re-creating original transformation matrix from eigenvalues & eigenvectors Creating Eigenvectors / Eigenvalues using Numpy In …
Sklearn SelectFromModel for Feature Importance
In this post, you will learn about how to use Sklearn SelectFromModel class for reducing the training / test data set to the new dataset which consists of features having feature importance value greater than a specified threshold value. This method is very important when one is using Sklearn pipeline for creating different stages and Sklearn RandomForest implementation (such as RandomForestClassifier) for feature selection. You may refer to this post to check out how RandomForestClassifier can be used for feature importance. The SelectFromModel usage is illustrated using Python code example. SelectFromModel Python Code Example Here are the steps and related python code for using SelectFromModel. Determine the feature importance using …
Sequential Forward Selection – Python Example
In this post, you will learn about one of feature selection techniques namely sequential forward selection with Python code example. Refer to my earlier post on sequential backward selection technique for feature selection. Sequential forward selection algorithm is a part of sequential feature selection algorithms. Some of the following topics will be covered in this post: Introduction to sequential feature selection algorithms Sequential forward selection algorithm Python example using sequential forward selection Introduction to Sequential Feature Selection Sequential feature selection algorithms including sequential forward selection algorithm belongs to the family of greedy search algorithms which are used to reduce an initial d-dimensional feature space to a k-dimensional feature subspace where k < d. …
Sequential Backward Feature Selection – Python Example
In this post, you will learn about a feature selection technique called as Sequential Backward Selection using Python code example. Feature selection is one of the key steps in training the most optimal model in order to achieve higher computational efficiency while training the model, and also reduce the the generalization error of the model by removing irrelevant features or noise. Some of the important feature selection techniques includes L-norm regularization and greedy search algorithms such as sequential forward or backward feature selection, especially for algorithms which don’t support regularization. It is of utmost importance for data scientists to learn these techniques in order to build optimal models. Sequential backward …
Pandas – Append Columns to Dataframe
In this post, you will learn different techniques to append or add one column or multiple columns to Pandas Dataframe (Python). There are different scenarios where this could come very handy. For example, when there are two or more data frames created using different data sources, and you want to select a specific set of columns from different data frames to create one single data frame, the methods given below can be used to append or add one or more columns to create one single data frame. It will be good to know these methods as it helps in data preprocessing stage of building machine learning models. In this post, …
LabelEncoder Example – Single & Multiple Columns
In this post, you will learn about LabelEncoder code examples for handling encoding labels related to categorical features of single and multiple columns in Python Pandas Dataframe. The following are some of the points which will get covered: Background What are labels and why encode them? How to use LabelEncoder to encode single & multiple columns (all at once)? When not to use LabelEncoder? Background When working with dataset having categorical features, you come across two different types of features such as the following. Many machine learning algorithms require the categorical data (labels) to be converted or encoded in the numerical or number form. Ordinal features – Features which has …
Pandas – Fillna method for replacing missing values
In this post, you will learn about how to use fillna method to replace or impute missing values of one or more feature column with central tendency measures in Pandas Dataframe (Python).The central tendency measures which are used to replace missing values are mean, median and mode. Here is a detailed post on how, what and when of replacing missing values with mean, median or mode. This will be helpful in the data preprocessing stage of building machine learning models. Other technique used for filling missing values is backfill or bfill and forward-fill or ffill. Before going further and learn about fillna method, here is the Pandas sample dataframe we will work with. It represents marks in …
Pandas Dataframe vs Numpy Array: What to Use?
In this post, you will learn about which data structure to use between Pandas Dataframe and Numpy Array when working with Scikit Learn libraries. As a data scientist, it is very important to understand the difference between Numpy array and Pandas Dataframe and when to use which data structure. Here are some facts: Scikit learn was originally developed to work well with Numpy array Numpy Ndarray provides a lot of convenient and optimized methods for performing several mathematical operations on vectors. Numpy array can be instantiated using the following manner: np.array([4, 5, 6]) Pandas Dataframe is an in-memory 2-dimensional tabular representation of data. In simpler words, it can be seen …
Visualize Decision Tree with Python Sklearn Library
In this post, you will learn about different techniques you can use to visualize decision tree (a machine learning algorithm) using Python Sklearn (Scikit-Learn) library. The python code example would use Sklearn IRIS dataset (classification) for illustration purpose. The decision tree visualization would help you to understand the model in a better manner. The following are two different techniques which can be used for creating decision tree visualisation: Sklearn tree class (plot_tree method) Graphviz library Sklearn Tree Class for Visualization In this section, you will see the code sample for creating decision tree visualization using Sklearn Tree method plot_tree method. Sklearn IRIS dataset is used for training the model. Here is …
Decision Tree Classifier Python Code Example
In this post, you will learn about how to train a decision tree classifier machine learning model using Python. The following points will be covered in this post: What is decision tree? Decision tree python code sample What is Decision Tree? Simply speaking, the decision tree algorithm breaks the data points into decision nodes resulting in a tree structure. The decision nodes represent the question based on which the data is split further into two or more child nodes. The tree is created until the data points at a specific child node is pure (all data belongs to one class). The criteria for creating the most optimal decision questions is …
How to Convert Sklearn Dataset to Dataframe
In this post, you will learn how to convert Sklearn.datasets to Pandas Dataframe. It will be useful to know this technique (code example) if you are comfortable working with Pandas Dataframe. You will be able to perform several operations faster with the dataframe. Sklearn datasets class comprises of several different types of datasets including some of the following: Iris Breast cancer Diabetes Boston Linnerud Images The code sample below is demonstrated with IRIS data set. Before looking into the code sample, recall that IRIS dataset when loaded has data in form of “data” and labels present as “target”. Executing the above code will print the following dataframe. In case, you don’t want to explicitly assign …
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 …
Google Technical Writing Course – Cheat Sheet
In this post, you will quickly learn about key learning from free course on Technical writing by Google. Define new or unfamiliar terms: When writing or editing, learn to recognize terms that might be unfamiliar to some or all of your target audience. If the term already exists, link to a good existing explanation. In case your document is introducing the term, define the term properly. Use acronyms properly: On the initial use of an unfamiliar acronym within a document or a section, spell out the full term, and then put the acronym in parentheses. Active voice vs Passive voice: Prefer active voice to the passive voice Clear Sentences Choose strong verbs …
Flutter Flexible Widget Example with Row
In this post, you will learn about how to use Flexible Widget to ensure equal spacing for children of Row. Flexible is a widget that controls how a child of a Row, Column, or Flex flexes. In this post, we will see how to use a Flexible widget to control the width of the children’s widget contained in the Row widget. Look at each of the rows in the app below consisting of 4 letters of the English Alphabet. In case we don’t use a Flexible widget, we may have to assign the width of each of the containers (represented using commented cell width in the code below. However, using a Flexible widget, there is no need for the …
Flutter Row Concepts with Code Example
In this post, you will learn about Flutter Row concepts with code example. Flutter Row is a flex widget that lets you create flexible layouts in the horizontal directions. The design of these objects is based on the web’s flexbox layout model. In other words, Row is a widget that displays its children in a horizontal array. In this post, you will see how to build a UI such as below using Flutter Row widget. In order to achieve above, a stateless widget namely AlphabetWidget is created whose build method is used to build the above UI widgets. Here is the code for AlphabetWidget. In the above code example, pay …
I found it very helpful. However the differences are not too understandable for me