Integrated Circuits: Chapter X

Python Automation and Machine Learning for ICs: Chapter X
- Python Automation and Machine Learning for ICs -
- An Online Book: Python Automation and Machine Learning for ICs by Yougui Liao -

Python Automation and Machine Learning for ICs http://www.globalsino.com/ICs/

Table of Contents/Index

Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix

=================================================================================

XGBoost (Extreme Gradient Boosting)

Introduction

Plot graph/figure/image from CSV file/DataFrame by removing/hiding blank/empty cells with axis range (plt.xlim())

Introduction

Comparison between decision tree, random forest and XGBoost (extreme gradient boosting)

Introduction

x-coordinate

(code)

__xor__

xlim

x-axis limits (e.g., [0, 10])

xy=

code.

xytext=

code.

.xlabel()

(code)

.xlim()

(code)

hotkey('x')

Introduction

from pptx.enum.chart import XL_CHART_TYPE

(code)

.set_xticks()

(code)

.set_xticklabels()

(code)

find_element(By.XPATH, "")

Introduction

Convert a CSV file to an image with one column and another column as x-axis and y-axis, respectively.

code.

Markers (e.g. color cross, scatter, and circles) at specific coordinates with x- and y-axis

Matplotlib

matplotlib.pyplot axis/text color (xticks, rotation, xlabel, ylabel, title, fontsize, grid(), legend(), show())

Introduction

=================================================================================