Niharika Deshpande

What is a NullPointerException, and how do I fix it?

A NullPointerException (NPE) in Java is a runtime error that occurs when you try to use an object reference that hasn’t been initialized (i.e., it's null). This usually happens when calling a method, accessing a field, or performing operations on a null object, and it’s one of the most common bugs Java developers run…

Java

Answered May 15, 2025 · 595 Views Read answer →

check upstream git-How can I see which Git branches are tracking which remote / upstream branch?

To resolve check upstream git you should follow: “[]@{push}, e.g. master@{push}, @{push}The suffix @{push} reports the branch "where we would push to" if git push were run while branchname was checked out (or the current HEAD if no branch name is specified). Since our push destination is in a remote repository, of…

SQL Server

Answered Jul 1, 2021 · 882 Views Read answer →

Explain how to use tf-idf in R?

We can implement in R after importing following librariesWe will perform the following operation on the doc sentence.We get the following output in the matrix form

Data Science

Answered Dec 24, 2019 · 1.1K Views Read answer →

A user is trying to run Naive Bayes in R for making predictions from textual data (by building a Document Term Matrix). But he received some weird predictions. Why?

The output is given belowTo avoid this issue, the Term Document Matrix or the TDM should get rid of too much sparsity. By adding the below code will fix the problem.

Data Science

Answered Dec 20, 2019 · 933 Views Read answer →

How to implement one-hot encoding in Python and in R?

One-hot encoding is used to transform the categorical variables into dummy variables which is used in order to fit in the model. In Python To implement one-hot encoding in Python, we use the following import numpy as np import matplotlib.pyplot as plt import pandas as pd # Importing the dataset dataset =…

Data Science

Answered Nov 30, 2019 · 1.2K Views Read answer →

Explain concordance with example.

It is a process of measuring quality of fit for a binary outcome in a model. It is a proportion of pairs in which the predicted event probability is higher for the actual event than non-event. Let us take the following example to explain the concept. In the following table, both actual and predicted values are shown…

Data Science

Answered Nov 30, 2019 · 2.2K Views Read answer →

How to merge two lists in R?Explain with example

Let us define two lists and merge it. A list can contain an array of elements of any data types be it integer, character or float. first_list = list(a = 1, b = 2, c = 3) second_list = list(a = 2, b = 3, c = 4) To merge it, we can apply mapply() mapply() is a function which can be applied to multiple lists or vector…

Data Science

Answered Nov 9, 2019 · 992 Views Read answer →

How to calculate the entropy of a term document in R?

For doing that, we can use the following function in R library("quanteda") textstat_entropy

Data Science

Answered Nov 9, 2019 · 1.1K Views Read answer →

Explain with a case study how to visualize the train and test data of a Decision Classification system in R

First we import and split the dataset # Classification template # Importing the dataset dataset = read.csv('Social_Network_Ads.csv') dataset = dataset[3:5] # Encoding the target feature as factor dataset$Purchased = factor(dataset$Purchased, levels = c(0, 1)) # Splitting the dataset into the Training set and Test set

Data Science

Answered Nov 7, 2019 · 1.4K Views Read answer →

A user is currently viewing the decision tree using the following code. Is there a way that we can export some calculated fields as output too?

For example, is it possible to display the sum of an input attribute at each node, i.e. sum of feature 1 from 'X' data array in the leaves of the tree. from sklearn import datasets iris = datasets.load_iris() X = iris.data[:] y = iris.target #%% from sklearn.tree import DecisionTreeClassifier…

Data Science

Answered Nov 7, 2019 · 859 Views Read answer →

Is cross entropy always larger than entropy?

import numpy as np p = [0.5, 0.2, 0.2, 0.1] q_1 = [0.4, 0.2, 0.2, 0.2] q = [0.4, 0.2, 0.2, 0.2] def cross_entropy(p, q): return np.sum(-1*np.multiply(p, np.log(q))) # Get cross entropy print(cross_entropy(p, q)) # Get entropy

Data Science

Answered Nov 4, 2019 · 1.0K Views Read answer →