### Machine Unlearning #2 (Regression)

Machine Unlearning is a series broken up into tiny, one-minute readable pieces to humor our ever-shortening attention span. Sharing the links to every single piece right below:

Regression is a popular machine learning technique used to predict target data based on a set of features. In classification, we train the system to assign distinctive labels to the object (Orange or Bananas in our previous example). Regression differs in the sense that here we are dealing with continuous variables.

Suppose that you have twenty thousand rupees with you and are planning to buy a new phone. You open up an e-commerce site and search for phones. A hundred results appear. You see names like Redmi, Oppo, Vivo, Realme, Asus, Nokia, and so on, all in your target range of ten thousand to twenty thousand. You are clearly confused. Therefore, you decide to have a deeper comparison.

You start plotting the prices of each phone and start noting down the features offered. As you watch closely, you realize that some features (like support for 4g and dual sim support) are offered by most of the phones while the presence or absence of some features affects the price. Models with additional features like full HD display and quad camera are priced above fifteen thousand while the others are at the lower end of the price range. Then you do a trade-off and decide on the phone that suits you best.

Now let us look at how we use similar plotting of continuous values to predict certain aspects of people. One of the favorite target groups for such regression analysis in our daily lives is, unfortunately, women. The character of a girl or a woman is often predicted by the time at which she goes out in the public.

Let us talk about our favorite random person, X. X is a college-going girl for now. If she is back home or hostel by six in the evening, she is supposed to be well mannered. If she stays out later than six or (god forbid) six-thirty, something is not right! For working women, we have benevolently pushed the boundaries to seven. Don’t even ask me about eight or later. When the brutal and horrific news of Nirbhaya came out several years ago, some people around me were wondering why she was out at that hour of the day. I could not agree with their line of thought at all. However, the prime convict Mukesh Singh had the same questions. Frankly, I do not think he is the sort of person you wanna hold up as your role model.

Timings are only one of the features employed to predict the character. Another common feature is the length of her skirt, with knees being a major benchmark. Another feature that holds interest is the length of your hair. It is interesting because this feature works in contrasting ways for men and women. The longer, the better for women. The shorter, the better for men.

How regressive, right?

### അത് എന്ത് കൊണ്ടായിരിക്കും?

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### Planet Perillamus

Planet Perillamus An excited Ethoruthan broadcasted his findings to the Inter Universe Lifeform Detection Council. ‘I have discovered life on another planet.’ ‘Oh not again, Mx Ethoruthan!’, the Chairman of the council, Dan Maraman, shot back.‘ This is the eleventh time you are making such a claim over the last six months. How many missions have we launched to verify your claim - and have even one bore any result? These voyages are damn expensive, you know.’ ‘Please hear me out, Mx Maraman! This is not like the previous cases. I have proof.’ ‘What proof?’ Senior agent Thengaenthu was intrigued. ‘Do I have permission to present my thoughts to the council?’ Ethoruthan asked Maraman, Dan. ‘Yeah! You may.’ the Chairman relented warily. ‘Okay here is the interesting part. The life forms on this planet have devised something called movies - where some of them write unreal descriptions about unreal persons, and someone else would behave like those unreal persons. These behaviors would be re

### Movie Review : The Cabinet of Dr. Caligari

Title : The Cabinet of Dr. Caligari Language : Silent Movie Year : 1920 Director : Robert Wiene Genre : Horror IMDB Link Watch movie on YouTube Lead Role :   Friedrich Feher, Werner Krauss The movie is widely acknowledged as one of the landmark revolutionary offerings from the long gone era when movies did not speak. It may be technically incorrect to call a silent film German, nevertheless it was made in Germany during a time period when the European nation was in turmoils after the devastating World War I. The story begins with a young man by the name of Francis starts narrating the hardships faced by him and his fiancee (Jane) and the very peculiar, even horrifying doings of a strange man, Dr. Caligari. Dr. Caligari owns a stall at a nearby exhibition, and on display is a somnambulist Caeser, who allegedly has slept for 23 straight years! The doctor awakens him, and he answers questions asked by the spectators. To the horror of the locals, his prophecies comes true. Mean