### Jezero Carter, Mars boarding pass : Future Mars Mission

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### Introduction to Web Scraping with Python￼

Web-scraping is an vital strategy, as often as possible utilised in a part of distinctive settings, particularly information science and information mining. Python is to a great extent considered the go-to dialect for web-scraping, the reason being the batteries-included nature of Python. With Python, you’ll be able make a basic scratching script in approximately 15 minutes and in beneath 100 lines of code. So regardless of utilisation, web-scraping could be a expertise that every Python software engineer must have beneath his belt.

Before we begin getting hands-on, we ought to step back and consider what is web-scraping, when ought to we utilize it, and when to maintain a strategic distance from utilising it.

As you as of now know, web-scraping could be a procedure utilized to naturally extricate information from websites. What’s important to get it is, web-scraping may be a to some degree unrefined strategy to extract information from different sources – regularly web pages. In the event that the engineers of web site are liberal sufficient to supply an API to extract data, that would be a parcel more steady and vigorous way to do get to the data. So, as a run the show of thumb, on the off chance that web site gives an API to programmatically recover their information, utilize that. In case an API isn’t accessible, as it were at that point utilize web-scraping.

Be beyond any doubt to moreover comply with any rules or confinements with respect to web scratching for each site you utilize, as a few don’t permit it. With that being clear, let’s hop right into the instructional exercise.

### Relationship between bias, variance, and test set MSE

In all three cases, the variance increases and the bias decreases as the method’s flexibility increases. However, the flexibility level corresponding to the optimal test MSE differs considerably among the three data sets, because the squared bias and variance change at different rates in each of the data sets.

Continue reading…### Balancing Act: Mastering Underfitting & Overfitting

Welcome to a journey through the delicate landscape of machine learning models! Today, we’re tackling **two notorious pitfalls: underfitting and overfitting**. Imagine you’re teaching a child to recognise animals. If you only show them pictures of small dogs, they might not recognise a large dog as a dog—that’s underfitting. The model is too simplistic and fails to capture the diversity of the concept.

On the other hand, if you teach them by showing every possible variety of dogs, including those in costumes, they might get confused when they see a plain dog without a costume. That’s overfitting. The model has learned the training data, including the noise and outliers, so well that it fails when presented with new, unseen data.

Aspect | Underfitting | Overfitting |
---|---|---|

Complexity | Too simple to capture the underlying patterns in the data. | Too complex, capturing noise as if it were a significant pattern. |

Flexibility | Not flexible enough to learn from data. | Too flexible, learns from both the noise and the signal in the data. |

Performance on Training Data | Poor, as it cannot model the training data well enough. | Excellent, as it can model the training data too well. |

Performance on New Data | Poor, as it fails to generalize the patterns from the training data. | Poor, as it fails to generalize due to learning noise and outliers. |

Error due to | Bias, as it makes assumptions that are too strong about the data. | Variance, as it takes into account the random fluctuations in the data. |

Typical Causes | Not enough model complexity, insufficient features, too strong regularization. | Too much model complexity, too many features, not enough regularization. |

Indicators | High bias, low variance. | Low bias, high variance. |

Solution | Increase model complexity, add more features, reduce regularization. | Simplify model, remove some features, increase regularization. |

In machine learning, underfitting happens when a model is too simple to capture the underlying trend of the data. It doesn’t perform well even on the training data. Think of it as trying to fit a straight line to a curve—it doesn’t work because the model isn’t complex enough to handle the reality of the data’s shape.

Overfitting, conversely, is when a model is so complex that it captures the noise along with the trend. It’s like a line that zigzags to hit every point—it might look perfect for the training data but is too erratic to make sensible predictions on new data.

Our goal is to find the sweet spot—a model complex enough to capture the true pattern of the data but not so complex that it gets distracted by the noise. This post will walk you through understanding these concepts with clear examples, practical tips, and visual aids to ensure your model is just right. So, whether you’re new to the field or honing your skills, let’s optimize your models for the real world!

### Difference Between Classification and Regression in Machine Learning

### Classification vs Regression

Classification predictive modeling problems are different from regression predictive modeling problems.

- Classification is the task of predicting a discrete class label.
- Regression is the task of predicting a continuous quantity.

### What are “ensemble methods”?

An ensemble methods are technique which uses multiple independent similar or different models and then combine them to produce improved results. Ensemble methods usually produces more accurate solutions than a single model would. The motivation for using ensemble models is to reduce bias or variance.

**For e.g.** A random forest is an ensemble of multiple decision trees.

### Entropy, Information gain, and Gini Index: Decision Tree

The decision tree algorithm is one of the widely used methods for inductive inference. It approximates discrete-valued target functions while being robust to noisy data and learns complex patterns in the data.

The family of decision tree learning algorithms includes algorithms like ID3, CART, ASSISTANT, etc. They are supervised learning algorithms used for both, classification and regression tasks. They classify the instances by sorting down the tree from root to a leaf node that provides the classification of the instance. Each node in the tree represents a test of an attribute of the instance and a branch descending from that node indicates one of the possible values for that attribute. So, classification of an instance starts at a root node of the tree, tests an attribute at this node, then moves down the tree branch corresponding to the value of the attribute. This process is then repeated for the subtree rooted at the new node.

### What is bias and variance in machine learning?

- Some models are too simplistic and ignore important relationships in the training data, which could have improved their predictions. Such models are said to have high
**bias**. When a model has high bias, its predictions are consistently off, at least for certain regions of the data if not the whole range. For example, if you try to fit a line to a scatter plot where the data appears to follow a curve-linear pattern, then you can imagine that we won’t have a good fit. Some parts of the plot, the line will fall below the curve and other parts it will be above it, awkwardly trying to follow the trajectory of a curve. Since the line traces out the model’s predictions, then we can see that when the line falls below the curve, the predictions are consistently lower than the ground truth, and vice versa. So when you think of the word bias, think of**predictions being consistently off**. High-bias models are said to**underfit**[to the training data], and as such the prediction error is high both on the training data and test data.

### Python Array Squaring: Simplify the Complex

**Unlocking the Puzzle of Sorted Arrays**

When programming intersects with problem-solving, each line of code we write is more than just instruction; it’s a strategic move in a grander game of logic and efficiency. Big names in tech, such as Google, Apple, and Microsoft, recognize this. They often challenge interviewees with problems that seem deceptively simple but are tests of ingenuity—like squaring a sorted array while keeping it sorted. It’s not just a question; it’s a riddle waiting for a solution.

**A Tantalizing Challenge**

Imagine a sequence of numbers, each a stepping stone from the least to the greatest, laid out before you. Your mission, should you choose to accept it, is to navigate these numbers, square them in their place, and maintain their graceful order. It’s a dance of digits where negative numbers threaten to step out of line once squared. How would you keep the rhythm, ensuring each number finds its new spot in this sorted array dance?

**The Dance of the Two Pointers**

Dancers in a ballroom move with grace, each step calculated and precise. In the world of arrays, our dancers are the pointers. One takes the lead at the array’s beginning, the other follows at the end. As the music of algorithms plays, they move towards each other in a choreography set by absolute values—comparing, squaring, and adding to the final sequence. When the dance ends, they have traversed the entire array, and the result is a beautiful crescendo of numbers, each in its rightful place.

**The Pythonic Way**

Within the realms of Python, this dance is both elegant and efficient. Here’s how you can conduct this ballet of numbers:

```
def sortedSquares(nums):
left, right = 0, len(nums) - 1
result = []
while left <= right:
if abs(nums[left]) > abs(nums[right]):
result.insert(0, nums[left] ** 2)
left += 1
else:
result.insert(0, nums[right] ** 2)
right -= 1
return result
# Example usage:
# sortedSquares([-3, -1, 0, 4]) yields [0, 1, 9, 16]
```

This snippet is your guide, a recipe for transforming an array with whispers of complexity into a symphony of simplicity. It’s Python’s way of embracing the challenge, running in linear time, and teaching us that even the trickiest problems have solutions that are as beautiful as they are smart.

**The Takeaway**

As you step away from this post, take with you the essence of problem-solving: the ability to see through the problem to its core and the creativity to apply a solution that is as efficient as it is elegant. Whether you’re in a technical interview or crafting your masterpiece of code, remember that every problem has a pathway to clarity, and it’s yours to discover.

**Closing Note**

If you’ve found this dance through the numbers intriguing, stay tuned. There are more puzzles to solve, more codes to crack, and more elegant solutions to uncover in the vast universe of programming. Keep coding, keep solving, and may your journey through the arrays be ever ascending.