Month: December 2023

MTH_522_PROJECT_1

MTH522_Project3_Report

Project 2 Corrected Report

MTH522_Project2_Report (1)

1. Time series analysis was done on the “economic indicators” data to identify underlying trends, seasonality, and cyclical behaviors.
2. By time variable, the data were arranged chronologically in the “Year” and “Month” columns.
3. I later created a single date time column by combining the ‘Year’ and ‘Month’ columns to show the time.
4. Additionally, time series plots were used to visualize the data in order to see trends, patterns, and seasonality in each economic indicator over time.
5. Then I used methods like seasonal decomposition to break down the time series into its constituent parts (trend, seasonality, and residual) in order to analyze the individual contributions.
6. A time series dataset is split into three parts using the seasonal decomposition technique: the trend, seasonality, and residual or error components.
7. Used forecasting models to project future values of economic indicators, such as ARIMA (AutoRegressive Integrated Moving Average). After that, I divided the data into test and training sets so that I could train the model and assess its effectiveness.
8. Evaluated the forecasting model’s accuracy using a metric such as Mean Absolute Error (MAE), and evaluated the model’s performance by comparing the predicted values with the test dataset’s actual values.

Today’s Lecture:

1. I’ve experienced When building decision trees, a technique called pre-pruning, sometimes called early halting, involves applying limitations to stop the tree’s growth before it reaches full maturity.
2. This is deciding whether to stop splitting nodes throughout the building of the tree based on predetermined standards, including imposing a maximum depth limitation, establishing a minimum sample requirement for node splitting, or necessitating a minimum quantity of samples in a leaf node.
3. Pre-pruning is primarily done to keep the tree from growing too complicated and overfitting the training set, which will eventually improve the model’s capacity to generalize to new, unobserved data.
4. However, in order to enhance overall model performance, post-pruning, also known as simple pruning, is extending a decision tree to its full depth before carefully deleting any branches or nodes.
5. After allowing the original tree to grow unrestrictedly, branches that do not substantially improve prediction accuracy are trimmed in accordance with predetermined standards.
6. The trade-off between the tree’s complexity and its fit to the training set is taken into account by common techniques like cost-complexity pruning. Post-pruning is primarily used to reduce overfitting and simplify the decision tree, especially in cases where the tree structure retains noise or training data-specific patterns that may not translate well to fresh data.
7. In conclusion, post-pruning removes portions of the tree whereas pre-pruning regulates tree growth throughout building.