Category Archives: Data Science
Handling Class Imbalance using Sklearn Resample
In this post, you will learn about how to tackle class imbalance issue when training machine learning classification models with imbalanced dataset. This is illustrated using Python SKlearn example. In the same context, you may check out my earlier post on handling class imbalance using class_weight. As a data scientist, it is of utmost importance to learn some of these techniques as you will often come across the class imbalance problem while working on different classification problems. Here is how the class imbalance in the dataset can be visualized: Before going ahead and looking at the Python code example related to how to use Sklearn.utils resample method, lets create an imbalanced data …
Micro-average & Macro-average Scoring Metrics – Python
In this post, you will learn about how to use micro-averaging and macro-averaging methods for evaluating scoring metrics (precision, recall, f1-score) for multi-class classification machine learning problem. You will also learn about weighted precision, recall and f1-score metrics in relation to micro-average and macro-average scoring metrics for multi-class classification problem. The concepts will be explained with Python code examples. What & Why of Micro and Macro-averaging scoring metrics? With binary classification, it is very intuitive to score the model in terms of scoring metrics such as precision, recall and F1-score. However, in case of multi-class classification it becomes tricky. The questions to ask are some of the following: Which metrics to use to score …
PyTorch – How to Load & Predict using Resnet Model
In this post, you will learn about how to load and predict using pre-trained Resnet model using PyTorch library. Here is arxiv paper on Resnet. Before getting into the aspect of loading and predicting using Resnet (Residual neural network) using PyTorch, you would want to learn about how to load different pretrained models such as AlexNet, ResNet, DenseNet, GoogLenet, VGG etc. The PyTorch Torchvision projects allows you to load the models. Note that the torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision. Here is the command: The output of above will list down all the pre-trained models available for loading and prediction. You may …
How to install PyTorch on Anaconda
This is a quick post on how to install PyTorch on Anaconda and get started with deep learning projects. As a machine learning enthusiasts, this is the first step in getting started with PyTorch. I followed this steps on Mac Air and got started with PyTorch in no time. Here are the steps: Go to Anaconda tool. Click on “Environments” in the left navigation. Click on arrow marks on “base (root)” as shown in the diagram below. It will open up a small modal window as down. Click open terminal. This will open up a terminal window. Execute the following command to set up PyTorch. Once done, go to Jupyter Notebook window and …
ROC Curve & AUC Explained with Python Examples
In this post, you will learn about ROC Curve and AUC concepts along with related concepts such as True positive and false positive rate with the help of Python examples. It is very important to learn ROC, AUC and related concepts as it helps in selecting the most appropriate machine learning models based on the model performance. What is ROC & AUC / AUROC? Receiver operating characteristic (ROC) graphs are used for selecting the most appropriate classification models based on their performance with respect to the false positive rate (FPR) and true positive rate (TPR). These metrics are computed by shifting the decision threshold of the classifier. ROC curve is used for probabilistic models …
Python – Nested Cross Validation for Algorithm Selection
In this post, you will learn about nested cross validation technique and how you could use it for selecting the most optimal algorithm out of two or more algorithms used to train machine learning model. The usage of nested cross validation technique is illustrated using Python Sklearn example. When it is about selecting models trained with a particular algorithm with most optimal combination of hyper parameters, you can adopt the model tuning techniques such as some of the following: Grid search Randomized search Validation curve The following topics get covered in this post: Why nested cross-validation? Nested cross-validation with Python Sklearn example Why Nested Cross-Validation? Nested cross-validation technique is used for estimating …
Randomized Search Explained – Python Sklearn Example
In this post, you will learn about one of the machine learning model tuning technique called Randomized Search which is used to find the most optimal combination of hyper parameters for coming up with the best model. The randomized search concept will be illustrated using Python Sklearn code example. As a data scientist, you must learn some of these model tuning techniques to come up with most optimal models. You may want to check some of the other posts on tuning model parameters such as the following: Sklearn validation_curve for tuning model hyper parameters Sklearn GridSearchCV for tuning model hyper parameters In this post, the following topics will be covered: What and why …
Grid Search Explained – Python Sklearn Examples
In this post, you will learn about another machine learning model hyperparameter optimization technique called as Grid Search with the help of Python Sklearn code examples. In one of the earlier posts, you learned about another hyperparamater optimization technique namely validation curve. As a data scientist, it will be useful to learn some of these model tuning techniques (tuning hyperparameters) as it would help us select most appropriate models with most appropriate parameters. The following are some of the topics covered in this post: What & Why of grid search? Grid search with Python Sklearn examples What & Why of Grid Search? Grid Search technique helps in performing exhaustive search over specified parameter (hyper parameters) values for …
Five Types of Commercial Real Estate Loans: Which Do You Need?
Before you start commercial real estate loan shopping, it’s important to know which commercial loans are right for your business. Commercial real estate loans are commercial mortgages that need to be repaid, like any other mortgage. These commercial property loans can have interest rates and terms that vary depending on the commercial property’s purpose or location. There are five commercial real estate loans that we will discuss in this blog post: – Commercial mortgage loan – Construction loan – Bridge loan – Hard money commercial mortgage loans – Business cash-out refinance Commercial mortgage loan A commercial mortgage loan is the most common commercial real estate loan. It’s also known as …
Validation Curves Explained – Python Sklearn Example
In this post, you will learn about validation curves with Python Sklearn example. You will learn about how validation curves can help diagnose or assess your machine learning models in relation to underfitting and overfitting. On the similar topic, I recommend you reading one of the previous post on assessing overfitting and underfitting titled Learning curves explained with Python Sklearn example. The following gets covered in this post: Why validation curves? Python Sklearn example for validation curves Why Validation Curves? As like learning curve, the validation curve also helps in diagnozing the model bias vs variance. The validation curve plot helps in selecting most appropriate model parameters (hyper-parameters). Unlike learning …
Python – 5 Sets of Useful Numpy Unary Functions
In this post, you will learn about some of the 5 most popular or useful set of unary universal functions (ufuncs) provided by Python Numpy library. As data scientists, it will be useful to learn these unary functions by heart as it will help in performing arithmetic operations on sequential-like objects. These functions can also be termed as vectorized wrapper functions which are used to perform element-wise operations. The following represents different set of popular functions: Basic arithmetic operations Summary statistics Sorting Minimum / maximum Array equality Basic Arithmetic Operations The following are some of the unary functions whichc an be used to perform arithmetic operations: add, subtract, multiply, divide, …
What, When & How of Scatterplot Matrix in Python
In this post, you will learn about some of the following in relation to scatterplot matrix. Note that scatter plot matrix can also be termed as pairplot. Later in this post, you would find Python code example in relation to using scatterplot matrix / pairplot (seaborn package). What is scatterplot matrix? When to use scatterplot matrix / pairplot? How to use scatterplot matrix in Python? What is Scatterplot Matrix? Scatter plot matrix is a matrix (or grid) of scatter plots where each scatter plot in the grid is created between different combinations of variables. In other words, scatter plot matrix represents bi-variate or pairwise relationship between different combinations of variables …
Logistic Regression Quiz Questions & Answers
In this post, you will learn about Logistic Regression terminologies / glossary with quiz / practice questions. For machine learning Engineers or data scientists wanting to test their understanding of Logistic regression or preparing for interviews, these concepts and related quiz questions and answers will come handy. Here is a related post, 30 Logistic regression interview practice questions I have posted earlier. Here are some of the questions and answers discussed in this post: What are different names / terms used in place of Logistic regression? Define Logistic regression in simple words? Define logistic regression in terms of logit? Define logistic function? What does training a logistic regression model mean? What are different types …
When to use LabelEncoder – Python Example
In this post, you will learn about when to use LabelEncoder. As a data scientist, you must have a clear understanding on when to use LabelEncoder and when to use other encoders such as One-hot Encoder. Using appropriate type of encoders is key part of data preprocessing in machine learning model building lifecycle. Here are some of the scenarios when you could use LabelEncoder without having impact on model. Use LabelEncoder when there are only two possible values of a categorical features. For example, features having value such as yes or no. Or, maybe, gender feature when there are only two possible values including male or female. Use LabelEncoder for …
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 …
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