This article Data Science Blogthon.
prologue
Voting ensemble is an ensemble machine learning technique that is one of the best performing models among all machine learning algorithms. Since voting ensembles are the most commonly used ensemble technique, there are many interview questions related to this topic in data science interviews.
In this article, we’ll explore some of the most commonly asked interview questions and illustrate the core intuition and working mechanics with code examples. By reading and practicing these questions, you will be able to answer interview questions related to voting ensembles very efficiently.
See here for different ensemble methods.
Let’s solve them one by one.
Interview questions about voting ensembles
1. What is a polling panel? how does that work?
A voting ensemble is a type of machine learning algorithm that falls under the ensemble method. Since they are one of the ensemble algorithms, they use multiple models to train and make predictions on the dataset.
These are two categories of voting ensembles.
- classification
- regression
Voting classifiers are ensembles used in machine learning classification tasks. A voting classifier expects multiple models of different machine learning algorithms to exist, fed the entire dataset, and all algorithms to be trained on the data. Once all the models have predicted the sample data, the most frequent strategy is used to get the final predictions from the models. Here, the category best predicted by multiple algorithms is treated as the model’s final prediction.
For example, if 3 models predict YES and 2 predict NO, then YES is considered the model’s final prediction.
A vote regressor is the same as a vote classifier. Still, they are used in regression problems and the final output from this model is the average of all individual model predictions.
For example, if the outputs from three models are 5, 10, and 15, the final result will be 15, the average of these values.
2. How can Voting Ensemble be implemented?
Several methods are used in which voting ensembles can be implemented.
classification
For classification problems, it can be implemented using the VotingClasssifier class directly. There is also a manual way to implement the same.
Using sklearn’s VotingClassifier class:
#Using VotingClassifier
from sklearn.ensemble import VotingClassifier
model1 = LogisticRegression(random_state=7)
model2 = tree.DecisionTreeClassifier(random_state=7)
model = VotingClassifier(estimators=[('lr', model1), ('dt', model2)])
model.fit(x_train,y_train)
regression
In regression, there are two ways to implement voting ensembles.
- Using the VotingRegressor class
- manual implementation
1. Using the VotingRegressor class:
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import VotingRegressor
from sklearn.neighbors import KNeighborsRegressor
r1 = LinearRegression()
r2 = RandomForestRegressor(n_estimators=10, random_state=1)
r3 = KNeighborsRegressor()
rer = VotingRegressor([('lr', r1), ('rf', r2), ('r3', r3)])
2. Manual implementation:
Normal averaging:
model1 = tree.DecisionTreeClassifier() model2 = KNeighborsClassifier() model3= LogisticRegression() model1.fit(x_train,y_train) model2.fit(x_train,y_train) model3.fit(x_train,y_train) pred1=model1.predict_proba(x_test) pred2=model2.predict_proba(x_test) pred3=model3.predict_proba(x_test) finalpred=(pred1+pred2+pred3)/3
weighted average:
model1 = tree.DecisionTreeClassifier() model2 = KNeighborsClassifier() model3= LogisticRegression() model1.fit(x_train,y_train) model2.fit(x_train,y_train) model3.fit(x_train,y_train) pred1=model1.predict_proba(x_test) pred2=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 for the improved performance of the voting ensemble?
There are multiple reasons behind the performance of voting ensembles. Here are the main reasons behind the excellent performance of voting ensembles. Even with a weak machine learning algorithm as a base model, voting ensembles work well because of the combined power of multiple algorithms.
2. Voting Classifier uses various machine learning algorithms. Some machine learning algorithms perform very well on certain types of data. (For example, some of them are robust against outliers, etc.). Since this process involves multiple models, all algorithms use their knowledge to solve different problems and patterns in the data set, and all algorithms try to solve the design of the data. So you can use different algorithms in your voting ensemble to learn multiple problems and practices in your data.
3. There is also an option to manually weight specific models. Let’s say you have a dataset with outliers. In this case, you can manually increase the weights of the algorithm. This gives better performance for outliers. Ultimately, this helps improve the performance of voting ensembles.
4. What are hard and soft voting?
The hard votes of the voting ensemble served as the final output, the predicted predictions from most algorithms. For example, if the base model maximum shows YES as an output, the final product will also say YES to him.
Soft voting is a process in which the probabilities of all predictions are considered, and the class with the highest total probability from all base models is considered the final output from the model. For example, if category YES is more likely than category NO as a prediction of all base models, the final prediction from the model will be YES.
5. How is ensemble voting different from other ensemble techniques?
The major differences between voting ensembles and other boosting algorithms are discussed below.
A voting ensemble is an ensemble technique that trains multiple machine learning models and outputs the combined predictions from all the individual models. Unlike voting ensembles, the same algorithm is used as the base model for techniques such as bagging and boosting. Also, unlike voting ensembles, weighting and aggregation steps are performed here. Stacking and blending are ensemble techniques involving layers of algorithms such as base models and metamodels. When base models are trained, the metamodel assigns weights to specific base models to improve the performance of the algorithm.
In this article, we’ll cover the top five interview questions related to Voting Ensembles and the core ideas and intuition behind them. By reading and practicing these questions, you will understand the mechanics behind voting ensembles and will be able to answer them effectively in interviews.
A few key insight From this article:
1. Voting ensembles are an ensemble technique that works well with weak machine learning algorithms. Also, ultimately all individual algorithms will have a combined power.
2. Faster than other ensemble methods due to less computational power.
3. If we want to consider the weights or probabilities of all categories in the output column, we can use the soft voting technique.
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