Ashish Sinha

closeddocker: “build” requires 1 argument. See 'docker build --help'

You are getting this error as you forgot to add the dot after 'build'. You need to type in the command like this : $ sudo docker build . -t < name>The dot basically tells docker that the dockerfile that has to be used is in the current directory. If you wish to specify a dockerfile that is not located in the…

Devops

Answered Apr 20, 2021 · 1.6K Views Read answer →

When normalizing the data set iris in python and get the error ::TypeError: 'numpy.float64' object is not callable

This is because you incorrectly normalizing, you need to do it like this: for i in names: print(i) c[i] = (c[i] - c[i].min())/(c[i].max() - c[i].min())Note: TypeError: 'float' object is not callable generally generates when values accidentally assigned to built-in function

Python

Answered Apr 17, 2021 · 1.2K Views Read answer →

Sqlite: CURRENT_TIMESTAMP is in GMT, not the timezone of the machine, how to fix it?

SQL Server

Answered Apr 1, 2021 · 1.2K Views Read answer →

How to import FastText from the gensim?

To import Fast Text, we need only one line of data to train the word embedding.Let us try a word to fit in the model and see in the similarityLet us see the similarity given by the model

Data Science

Answered Dec 24, 2019 · 1.7K Views Read answer →

Explain with a case study how to visualize a KNN classification model in Python.

First we import the libraries and the dataset # Importing the libraries import numpy as np import matplotlib.pyplot as plt import pandas as pd # Importing the dataset dataset = pd.read_csv('Social_Network_Ads.csv') X = dataset.iloc[:, [2, 3]].values y = dataset.iloc[:, 4].values

Data Science

Answered Dec 20, 2019 · 1.3K Views Read answer →

Why is F1-score considered a better evaluation technique compared to others?

F1-Score is the harmonic mean of precision and recall. Multiplying the constant of 2 scales the score to 1 when both precision and recall are 1. Precision works fine when false positive cases are high. On the other hand, recall works fine when false negative cases are high. Since many domain cases such as medical,…

Data Science

Answered Nov 30, 2019 · 1.3K Views Read answer →

Explain Grid Search with example and code.

Grid search is an algorithm used for tuning a model by considering every hyperparameters specified and used for evaluating a model. It is used to determine the optimal values for a given model. To implement Grid Search in Python, we can use the following code. # Applying Grid Search to find the best model and the best…

Data Science

Answered Nov 30, 2019 · 1.3K Views Read answer →

Explain the concept of logistic regression.

Logistic regression is a very popularly utilized classification technique in the credit and risk industry for checking the probability of default problems. The challenges which are faced nowadays by credit and risk departments are due to the black box nature of machine learning models, which is the prime reason of…

Data Science

Answered Nov 28, 2019 · 1.1K Views Read answer →

What are the libraries used in Named Entity Recognition?

NLP provided some of the developed libraries to implement Named Entity Recognition. Some of them are a) Nltk b) Spacy c) Stanford NER

Data Science

Answered Nov 26, 2019 · 971 Views Read answer →

What are the advantages and disadvantages of decision tree algorithm?

Advantages Decision tree is comprehensive in nature as it forces the consideration of all possible outcomes of a decision and traces to a conclusion In monetary problems where decision tree assigns specific values to attain an outcome, this algorithm reduces uncertainty, clears up ambiguity and clarifies the financial…

Data Science

Answered Nov 9, 2019 · 1.3K Views Read answer →

How to perform ROC curve in R?

To perform ROC on a model we need to train a model. bank

Data Science

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

Explain Information gain

Information gain is a measure of reduction in entropy caused by partitioning the examples according to a given attribute. The idea is to start with mixed classes and to continue partitioning until each node reaches its observations of purest class. At every stage, the variable with maximum information gain is chosen…

Data Science

Answered Nov 4, 2019 · 1.3K Views Read answer →