This article was published as a part of the Data Science Blogathon.
Voting Ensemble is a machine learning technique, which is one of the best performing models among all machine learning algorithms. Since polling ensemble is the most commonly used ensemble technique, there are a lot of interview questions related to this topic which are asked in data science interview.
In this article, we will explore the top interview questions related to polling groups and answer them along with their basic intuition and working mechanism along with code examples. Reading and practicing these questions will help in answering the interview questions related to the polling group very efficiently.
Let’s start solving them one by one.
Interview Questions on Voting Ensemble 1. What is Voting Ensemble? How does this work?
Voting ensembles are types of machine learning algorithms that fall under ensemble techniques. As they are one of the ensemble algorithms, they use multiple models to train on the dataset and for predictions.
They are the two categories of the voting ensemble.
Voting classifiers are ensembles used in classification tasks in machine learning. In voting classifiers, there exist multiple models of different machine learning algorithms, which are fed the complete dataset, and each algorithm will make predictions after being trained on the data. Once all the models predict the sample data, the most frequent strategy is used to obtain the final prediction from the model. Here, the category most predicted by many algorithms will be considered as the final prediction of the model.
For example, if three models predict yes and two models predict no, then yes will be considered the final prediction of the model.
Voting registers are similar to voting classifiers. Nevertheless, they are used on regression problems, and the final output from this model is the predicted mean of all the individual models.
For example, if the outputs of the three models are 5, 10 and 15, the final result will be the mean of these values, which is 15.
2. How Voting Ensemble can be implemented?
Several methods are used in which voting groups can be implemented.
For the classification problem, the voting classifier class can be directly implemented, and there is also a manual method to implement it.
Using the VotingClassifier class from Sklearn:
# Using the voting classifier from sklearn.ensemble import[(‘lr’, model1), (‘dt’, model2)]) model fit(x_train, y_train)
In regression, there are two ways to implement a polling group.
Using VotingRegressor Class Manual Implementation
1. Using the VotingRegressor Class:
Import numpy from sklearn as np. err = VotingRegressor([(‘lr’, r1), (‘rf’, r2), (‘r3’, r3)],
2. Manual Implementation:
Model 1 = Tree. decision tree classifier() model 2 = kNeighbors classifier() model 3 = logistic regression() model 1. fit(x_train, y_train) model 2. fit(x_train, y_train) model 3. fit(x_train, y_train) pred1 = Model 1. Predict_proba(x_test) pre2 =model2.predict_proba(x_test) pred3=model3.predict_proba(x_test) finalpred=(pred1+pred2+pred3)/3
Model 1 = Tree. decision tree classifier() model 2 = kNeighbors classifier() model 3 = logistic regression() model 1. fit(x_train, y_train) model 2. fit(x_train, y_train) model 3. fit(x_train, y_train) pred1 = Model 1. Predict_proba(x_test) pre2 =model2.predict_proba(x_test) pred3=model3.predict_proba(x_test) finalpred=(pred1*0.3+pred2*0.3+pred3*0.4) 3. What is the reason behind the better performance of polling parties Is? There are many reasons behind the performance of polling parties. The primary reasons behind the excellent performance of the polling parties are listed below.1. Voting ensembles still work well if there are weak machine learning algorithms as a base model because of the power of multiple algorithms combined.
2. Various machine learning algorithms are used in the voting classifier. Now, few machine learning algorithms perform so well on certain types of data. (e.g. some of them are robust to outliers, etc.). So since the process involves multiple models, the knowledge of each algorithm will be used to solve different problems and patterns of the dataset, and each algorithm will try to solve the design of the data. So many issues and practices of data can be learned by using different algorithms in Voting Ensemble.
3. In Voting Ensemble, there is also an option to give weightage to particular model manually. Let’s say we have a dataset with outliers, so in this case, we can manually increase the weights of the algorithm, which performs better on outliers, so ultimately, it helps the voting ensemble to perform better .
4. What are Hard Voting and Soft Voting in Voting Ensembles
Hard voting in the voting ensemble, the prediction made by most algorithms, serves as the final output. For example, if the maximal base model indicates yes as the output, then the final product will also be yes.
Soft voting is the process in which each prediction probability is considered, and the class with the highest total chance from all base models is considered the final output from the model. For example, if the probability category of YES as a prediction for each base model is greater than NO, then YES will be the final prediction from the model.
5. How Voting Ensembles differ from other assembly technologies.
Voting ensemble is a technique that trains multiple machine learning models, and then the predictions from all the different models are combined for the output. Unlike voting ensembles, techniques such as bagging and boosting use the same algorithm as the base model. Furthermore, unlike the voting ensemble, the weighting and aggregation steps are performed here.
Whereas stacking and blending are techniques that have layers of algorithms, such as a base model and a meta-model. Where the base model is trained, the meta-model assigns weights to a particular base model for better algorithmic performance.
This article discusses the top 5 Interview Questions related to Voting Ensembles with the basic idea and intuition behind it. Reading and practicing these questions will help in understanding the mechanism behind the voting group and help in answering effectively in the interview.
Here are some key insights from this article:
1. Voting Ensembles are technologies that perform well with weak machine-learning algorithms. Also, in the end, each individual algorithm will have combined power.
2. Voting ensembles are faster than other ensemble techniques because less computational power is involved.
3. Soft voting technique can be used when we want to consider the weight or probability of each category in the output column.
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