Tag Archives: machine learning

Feature Extraction using PCA – Python Example

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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 …

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Sklearn SelectFromModel for Feature Importance

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 …

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Sequential Forward Selection – Python Example

Sequential forward selection algorithm

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. …

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Sequential Backward Feature Selection – Python Example

Sequential Backward Search for Feature Selection

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 …

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MinMaxScaler vs StandardScaler – Python Examples

MinMaxScaler vs StandardScaler

In machine learning, MinMaxscaler and StandardScaler are two scaling algorithms for continuous variables. The MinMaxscaler is a type of scaler that scales the minimum and maximum values to be 0 and 1 respectively. While the StandardScaler scales all values between min and max so that they fall within a range from min to max. In this blog post, you will learn about concepts and differences between MinMaxScaler & StandardScaler with the help of Python code examples. Note that these are classes provided by sklearn.preprocessing module and used for feature scaling purposes. As a data scientist, you will need to learn these concepts in order to train machine learning models using …

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Pandas – Append Columns to Dataframe

Append columns to the data frame

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, …

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LabelEncoder Example – Single & Multiple Columns

LabelEncoder for converting labels to integers

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 …

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Pandas – Fillna method for replacing missing values

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 …

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Pandas Dataframe vs Numpy Array: What to Use?

Pandas Dataframe vs Numpy Array

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 …

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Visualize Decision Tree with Python Sklearn Library

Decision tree visualization using GraphViz

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 …

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Decision Tree Classifier Python Code Example

Decision tree decision boundaries

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 …

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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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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 …

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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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