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Category: Machine Learning

Data Analysis

Precision-Recall Curve for Classification model analysis

October 15, 2020October 15, 2020 No Comments

Accuracy is something which gives an intuition of model performance. i.e. ratio of number of correct predictions with respect to total sample present. But what in case of unbalanced data.Imagine a case where we need to make a model to predict click through rate over a display rate. The click trough rate used to be […]

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Machine Learning

The ROC – AUC curves for Classification based Model Performance Analysis

October 13, 2020October 13, 2020 No Comments

Accuracy is something which gives an intuition of model performance. i.e. ratio of number of correct predictions with respect to total sample present. But what in case of unbalanced data.Imagine a case where we need to make a model to predict click through rate over a display rate. The click trough rate used to be […]

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Machine Learning

Logistic Regression using Python

July 27, 2020October 17, 2020 No Comments

Logistic Regression is a method to create Machine Learning model for two class problems. It came out of linear regression but used to generate binary output (0 and 1) for making classifications. For example In Linear Regression we use simple linear equation as follows :- Yh = b0 + b1X1 Where X combines linearly with […]

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Machine Learning

Decision Tree Algorithm and Gini Index using Python

October 31, 2018October 17, 2020 No Comments

Decision Tree Classification Algorithm is used for classification based machine learning problems. It is one of the most popular algorithm as the final decision tree is quite easy to interpret and explain. More advanced ensemble methods like random forest, bagging and gradient boosting are having roots in decision tree algorithm. Here we will try to […]

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Machine Learning

Diving into Linear Regression Models

October 31, 2018October 17, 2020 1 Comment

If we figured out our problem needs a regression approach to form a predictive model then generally we adopt linear model to start. Why ? The are easy to interpret, They get trained quickly, Optimization is easy and quite better etc. Editorial Team

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Machine Learning

KNN Algorithm and Maths behind

October 31, 2018October 17, 2020 No Comments

KNN or K – Nearest Neighbours is one the powerful algorithm used in classification based problems to successfully make categorical predictions. Scikit-Learn gives us built in library to use and make the process easier for us if we are having data. But here we will write KNN code mathematically without any inbuilt library to figure […]

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Machine Learning

Stopping Criteria for Decision tree Algorithm and tree Plots

October 31, 2018October 17, 2020 No Comments

Decision Tree is an algorithm build for Machine learning purposes which works on the concepts of dividing data into subsets. It means it works to give you subsets representing only one type of category or values within particular range. But there are certain drawback that need to be discussed. Editorial Team

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Machine Learning

ML Algorithms – Which one is better ?

October 31, 2018October 17, 2020 No Comments

In every situation we went through numerous option same as here. Whenever we decided to work on ML, we do stuck into a thought process of selecting an algorithm which suits our need. Its also bit complicated to know all the minor details of each one of them. Then how can we decide X is […]

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Data Analysis

Dimensionality Reduction – (PCA)

October 31, 2018October 18, 2020 2 Comments

Variations in the dataset is actually the information from the dataset and this is what the PCA uses. In simple terms PCA or Principal component analysis is a process to emphasise variations in a data set and generate strong pattern out of it. We can figure out the whole concepts in 3 points as follows— […]

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Data Analysis

Mean, Variance, Standard Deviation, Standard Score, Covariance & Data Projection

October 31, 2018October 17, 2020 1 Comment

Variance – It is the measure of squared difference from the Mean. To calculate it we follow certain steps mentioned below: Calculate average of numbers For each numbers subtract the mean and square the result Calculate the average of those squared differences i.e. Variance Editorial Team

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  • The ROC – AUC curves for Classification based Model Performance Analysis
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