#random forest

[where-is-carl-now]

He’s in the forest. He often finds a lot to do there, so he’ll probably be in there for a while. Update tomorrow.

-Paul.

[humanxbeing]

"A hundred plus years, and there's only one thing I've ever been sure of - you."

► fandom: buffy the vampire slayer ► character(s): spike + buffy ► song: awakening (random forest)

Blocked? Watch it here: https://vimeo.com/311160056

I own neither the footage nor music and make no profit.

----------------------------------------­--------------

Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "fair use" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.

#JamesMarsters #SarahMichelleGellar #Spuffy #BtVS

[njabeltin]

Running a Random Forest

A Random forest analysis was performed to evaluate the importance of a set of explanatory variables in predicting a binary categorical response variable, high versus low life expectancy. The explanatory variables included in the model were income per person, urban rate, and internet use rate.

To run the analysis, we applied the following syntax in Spyder.

The analysis run successfully and the results obtained are presented below.

The Results indicate that, internet use rate had the highest relative importance score, followed by income per person and urban rate. This indicates that internet access was the strongest contributor to distinguishing between countries with high and low life expectancy, while income and urbanization also played meaningful but smaller roles in prediction.

The random forest model achieved an overall classification accuracy of 78%, indicating good predictive performance in classifying countries into life expectancy categories. The confusion matrix showed that the model correctly classified most observations in both groups, suggesting that the selected socioeconomic variables are useful predictors of life expectancy. These findings highlight that non-linear relationships exist between the explanatory variables and life expectancy, and that internet connectivity appears to be the most influential factor in predicting higher life expectancy outcomes.

[jskdr]

I found that sometimes machine learning algorithms are very useful than most recent AI methods such as a large language model. Recently, I learn gradient boosting algorithms which is one of ensemble algorithm especially based on decision tree. Decision tree is different from other algorithm since it does not rely on the gradient descent learning algorithm. Gradient descent algorithm updates parameters progressively to reduce output of a given performance cost function. However, decision tree search the best breaking point of one of input features. For example, if we consider the dimension of house to predict its price, which dimension value is appropriate to break left and right side most equally. Here, the equal means genie purity. The left side of data cases and the right side of data cases should have the most similar average loss values. Then, we believe that break point is the best for this input feature. However, this tree would not be the best tree unless it goes deeper and deeper to consider detailed break point scenarios. Even if we apply deeper approach for the decision tree, its performance can not be the best. So, to improve the performance of this decision tree case, we consider ensemble learning by combining the output of multiple decision trees which are updated by different data sampling or pre-processing.

One of the famous ensemble algorithms is random forest which trains small decision tree models, or week model, in parallel. Then, we combine their output using voting for a classification problem. Here, we train different week models using different sampling of all data. Because of sampling, around 70% data are extracted from the original box and other 30% are taken as repeated samples. Because of this randomized dataset, each week decision tree operates differently once their trains are completed.

Gradient boost is another type of ensemble algorithm, which is boosting approach while random forest is a bagging approach.

[uplatz-blog]

🏷 AI Models Explained: Random Forest

📖 Random Forest is one of the most powerful yet practical AI models. It combines multiple Decision Trees to make predictions that are more accurate, stable, and robust than any single tree could achieve alone.

1️⃣ The Foundations

An ensemble learning technique that builds many Decision Trees during training.

Each tree makes an independent prediction, and the final output is the majority vote (for classification) or average (for regression).

Reduces variance and overfitting, common in single Decision Trees.

2️⃣ Where It’s Used

Finance: Credit scoring, fraud detection.

Healthcare: Disease prediction, diagnostic systems.

E-commerce: Product recommendations, customer churn prediction.

Operations: Forecasting demand or resource allocation.

3️⃣ Strengths vs Limitations

Strengths

Excellent accuracy on diverse datasets.

Handles missing values and outliers well.

Reduces overfitting compared to single trees.

Limitations

Less interpretable than individual trees.

Requires more computation and memory.

Harder to visualize due to many trees.

4️⃣ Pro Tips

Use fewer trees for faster performance; more trees for higher accuracy.

Tune parameters like n_estimators and max_depth for optimal results.

Use feature importance to understand which variables matter most.

💡 Final Note Random Forest brings balance between simplicity and power — improving predictions while maintaining reliability. It’s a must-know model for anyone working in machine learning or applied AI.

📌 Series Continuation This is Day 5 of the AI Models Explained series 🎉. Next up: Gradient Boosting – The Secret to Supercharged Model Accuracy.

Stay tuned with Uplatz as we continue exploring AI models, one at a time 🚀

[bytetrending]

Ensemble Methods: Boost Your Machine Learning

Understanding Random Forests Random forests are built upon the idea of creating multiple decision trees independently. Each tree is trained on a random subset of the data and a random selection of features. The final prediction is made by averaging (for regression) or voting (for classification) across all the individual trees. This approach introduces diversity into the model, reducing the risk…

Ensemble Methods: Boost Your Machine Learning

[thebandcampdiaries]

Noir Pixel presents: Random Forest

Noir Pixel has recently released a brand new studio work that sets the bar higher when it comes to channeling a broad range of influences. This new release is titled Random Forest, and it stands out for its distinctive sonic formula, which combines alt-rock aesthetics with alternative influences and some experimental sounds.

The opening song (title track, featuring Samtar) combines influences such as Rage Against The Machine and Alice In Chains into a truly distinctive personality. The bass and guitars are heavy and powerful, while the drums add an intricacy to the groove. The vocals are just as powerful, matching the intensity of the song. “Fully on-Site” brings more melodic twists to the EP, and some amazing female vocal parts that add an ethereal twist to the mix.

“WiFi Pangea” (with Jaydi Zavala) also brings a different flavor, with a nod to late 70s and early 80s acts such as Blondie or The Cure, especially in the melodies and the cool clean guitar riffs, which are reminiscent of Robert Smith & Co! Still, the song retains the weight of the previous tracks on the EP, while accentuating the melodies. “Preferential Attachment” leans heavily into the artist’s hip-hop roots and it features a guest artist, JuztKP, who does a great job at bringing vibe and flavor with his delivery, matching the groove of the band and floating on with the rhythm. The last song, “Doot Again,” is a perfect conclusion. It also features Jaydi Zavala, and it has a mellow, dark tone, up until a very unexpected switch at about 1:35, which really takes the song to a different place! Somewhere in between the ethereal sounds of Massive Attack and the pop leanings of Billie Eilish, this song is really a masterpiece of variety, highlighting Noir Pixel’s unpredictable style.

Ultimately, the EP channels the energy of alternative rock, but it also boasts a more melodic side, which makes the song feel quite diverse, making this a uniquely dynamic experience.

This is highly recommended to fans of artists as diverse as Radiohead, TOOL, Rage Against The Machine, Dead Rituals or Bilie Eilish, among others!

[canthandlethishit]

building a random forest tmr to predict hospital readmission rates, to draft practical solutions for this rando hospital to lower their readmission rate which based on the dataset is 46% woah

also theres 1-2 people whos visited the hospital for inpatient care like 15 days a year, and 1 person who’s went through ER 64 times in the year are they good