Explain briefly about Decision Trees and Random Forest predictive Models to your data set and see if they work. Provide screenshots as necessary.
Sample solution
Dante Alighieri played a critical role in the literature world through his poem Divine Comedy that was written in the 14th century. The poem contains Inferno, Purgatorio, and Paradiso. The Inferno is a description of the nine circles of torment that are found on the earth. It depicts the realms of the people that have gone against the spiritual values and who, instead, have chosen bestial appetite, violence, or fraud and malice. The nine circles of hell are limbo, lust, gluttony, greed and wrath. Others are heresy, violence, fraud, and treachery. The purpose of this paper is to examine the Dante’s Inferno in the perspective of its portrayal of God’s image and the justification of hell.
In this epic poem, God is portrayed as a super being guilty of multiple weaknesses including being egotistic, unjust, and hypocritical. Dante, in this poem, depicts God as being more human than divine by challenging God’s omnipotence. Additionally, the manner in which Dante describes Hell is in full contradiction to the morals of God as written in the Bible. When god arranges Hell to flatter Himself, He commits egotism, a sin that is common among human beings (Cheney, 2016). The weakness is depicted in Limbo and on the Gate of Hell where, for instance, God sends those who do not worship Him to Hell. This implies that failure to worship Him is a sin.
God is also depicted as lacking justice in His actions thus removing the godly image. The injustice is portrayed by the manner in which the sodomites and opportunists are treated. The opportunists are subjected to banner chasing in their lives after death followed by being stung by insects and maggots. They are known to having done neither good nor bad during their lifetimes and, therefore, justice could have demanded that they be granted a neutral punishment having lived a neutral life. The sodomites are also punished unfairly by God when Brunetto Lattini is condemned to hell despite being a good leader (Babor, T. F., McGovern, T., & Robaina, K. (2017). While he commited sodomy, God chooses to ignore all the other good deeds that Brunetto did.
Finally, God is also portrayed as being hypocritical in His actions, a sin that further diminishes His godliness and makes Him more human. A case in point is when God condemns the sin of egotism and goes ahead to commit it repeatedly. Proverbs 29:23 states that “arrogance will bring your downfall, but if you are humble, you will be respected.” When Slattery condemns Dante’s human state as being weak, doubtful, and limited, he is proving God’s hypocrisy because He is also human (Verdicchio, 2015). The actions of God in Hell as portrayed by Dante are inconsistent with the Biblical literature. Both Dante and God are prone to making mistakes, something common among human beings thus making God more human.
To wrap it up, Dante portrays God is more human since He commits the same sins that humans commit: egotism, hypocrisy, and injustice. Hell is justified as being a destination for victims of the mistakes committed by God. The Hell is presented as being a totally different place as compared to what is written about it in the Bible. As a result, reading through the text gives an image of God who is prone to the very mistakes common to humans thus ripping Him off His lofty status of divine and, instead, making Him a mere human. Whether or not Dante did it intentionally is subject to debate but one thing is clear in the poem: the misconstrued notion of God is revealed to future generations.
References
Babor, T. F., McGovern, T., & Robaina, K. (2017). Dante’s inferno: Seven deadly sins in scientific publishing and how to avoid them. Addiction Science: A Guide for the Perplexed, 267.
Cheney, L. D. G. (2016). Illustrations for Dante’s Inferno: A Comparative Study of Sandro Botticelli, Giovanni Stradano, and Federico Zuccaro. Cultural and Religious Studies, 4(8), 487.
Verdicchio, M. (2015). Irony and Desire in Dante’s” Inferno” 27. Italica, 285-297.
Sample Answer
Sample Answer
Decision Trees and Random Forest Predictive Models
Decision Trees
A Decision Tree is a supervised machine learning algorithm used for classification and regression tasks. It splits the data into subsets based on the value of input features, creating a tree-like model of decisions. Each internal node of the tree represents a feature, each branch represents a decision rule, and each leaf node represents an outcome (classification or predicted value).
Advantages of Decision Trees:
1. Interpretability: Easy to understand and interpret, as they mimic human decision-making.
2. No Scaling Required: They do not require feature scaling or normalization.
3. Handles Both Types of Data: Capable of handling both categorical and numerical data.
Disadvantages of Decision Trees:
1. Overfitting: Prone to overfitting, especially with complex trees.
2. Instability: Small changes in data can result in a completely different tree structure.
Random Forest
Random Forest is an ensemble learning method that constructs multiple decision trees during training and outputs the mode of their predictions (classification) or the mean prediction (regression). It introduces randomness by selecting a subset of features at each split, which helps improve model accuracy and control overfitting.
Advantages of Random Forest:
1. Higher Accuracy: Generally provides better accuracy than individual decision trees.
2. Robustness: More robust to overfitting due to averaging across multiple trees.
3. Feature Importance: Can provide insights into feature importance through its structure.
Disadvantages of Random Forest:
1. Complexity: More complex and less interpretable than single decision trees.
2. Resource-Intensive: Requires more computational resources for training and prediction.
Application on a Dataset
To demonstrate the use of Decision Trees and Random Forest models, we will walk through a Python example using the scikit-learn library on a sample dataset (e.g., the Iris dataset).
Step-by-Step Implementation
1. Import Libraries:
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report
2. Load Dataset:
iris = load_iris()
X = iris.data
y = iris.target
3. Split Data into Training and Testing Sets:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
4. Train Decision Tree Model:
dt_model = DecisionTreeClassifier(random_state=42)
dt_model.fit(X_train, y_train)
dt_predictions = dt_model.predict(X_test)
5. Evaluate Decision Tree Model:
print(“Decision Tree Accuracy:”, accuracy_score(y_test, dt_predictions))
print(classification_report(y_test, dt_predictions))
6. Train Random Forest Model:
rf_model = RandomForestClassifier(n_estimators=100, random_state=42)
rf_model.fit(X_train, y_train)
rf_predictions = rf_model.predict(X_test)
7. Evaluate Random Forest Model:
print(“Random Forest Accuracy:”, accuracy_score(y_test, rf_predictions))
print(classification_report(y_test, rf_predictions))
Screenshots
Since I cannot generate screenshots directly within this environment, you can run the above code in your local Python environment (Jupyter Notebook or any IDE) to visualize the output results and accuracy metrics for both models.
Conclusion
Both Decision Trees and Random Forest models are powerful tools for predictive modeling. Decision Trees provide easy interpretability, while Random Forest enhances performance and robustness by leveraging multiple trees. Depending on your specific needs—such as interpretability versus accuracy—you can choose the model that best suits your project requirements.
