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Based on analyst ratings, Flora Growth's 12-month average price target is $1.50. What is FLGC's upside potential, based on the analysts' average price target? Flora Growth has 711.69% upside potential, based on the analysts' average price target.

AXN was an Indian pay television channel that was owned by Sony Pictures Networks. Funded through advertising and subscription fees, AXN primarily focused on action genre and reality programming.

AXN launched in the Indian subcontinent on 8 January 1999 as a flagship feed of AXN Asia.[1] During the channel's early years it operated as a genre-based network, airing mainly action films, action programmes, action animation and action sports.

Facing tough competition from rival networks Zee Café and Star World and a precipitous decline in the international syndication market for fictional action series and films, the channel shifted in mid-2005 by gradually incorporating more and more drama and comedy based programs into its schedule in order to attract more viewers and replicate the success shows such as Friends (comedy) and Law & Order (drama) had found on other channels. Big acquisitions included 24 and the CSI franchise.[2]

The channel's high-definition feed was launched on 6 April 2015,[3] which carried the same schedule as the SD feed but with different commercials.[citation needed]

The channel underwent a rebranding on 24 January 2016 with the addition of the slogan Live R.E.D standing for Reality, Entertainment and Drama. During the premiering Billions with this transformation, the channel sought to further diversify its portfolio away from its action genre by airing action shows with more intense, smart and unexpected characters.[4]

To further its mission of diversification, the channel forged a multi-year deal with CBS Studios in July 2014, acquiring exclusive broadcast rights to the network's shows in India.[5] The acquisition of the critically acclaimed Hulu original series The Handmaid's Tale for first airing in India also helped greatly in attracting more viewers for the channel and advertising the channel's content.[citation needed]

The channel's viewership began to gradually decline since early 2019, post the implementation of the new tariff order (NTO) by the Telecom Regulatory Authority of India which mandated that channels should not be bundled in packs, leading to a sharp fall in demand for niche channels.[6]

Consequently, on 1 June 2020 Sony Pictures Networks India announced that effective from 30 June 2020 AXN would cease all operations in India for both its SD and HD feeds, with the majority of its programming moving to Sony's SVOD service, Sony Liv,[7] thus ending the channel's existence of 21 years.

Fall in ad revenue during the COVID-19 pandemic and further decline in viewership due to the advent of SVOD platforms in India were also cited as reasons behind the channel's closure.[6]

AXN primarily aired shows from the U.S. and U.K. (including selected shows produced/distributed by Sony Pictures Television). The following is a list of programming broadcast by the network:

Just For You The Bread House

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Dimensionality reduction is an important technique in data analysis and machine learning that allows us to reduce the number of variables in a dataset while retaining the most important information. By reducing the number of variables, we can simplify the problem, improve computational efficiency, and avoid overfitting.

Two popular dimensionality reduction techniques are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Both techniques aim to reduce the dimensionality of the dataset, but they differ in their objectives, assumptions, and outputs. But how do they differ, and when should you use one method over the other? As data scientists, it is important to get a good understanding around this concept as it is used in building machine learning models. Keep reading to find out with the help of Python code & examples.

In this blog post, we will compare and contrast the differences between PCA and LDA, and provide examples that illustrate these differences. We will discuss the implications of these differences for selecting between PCA and LDA in different contexts, and provide practical guidance on how to implement these techniques using Python.

Principal Component Analysis (PCA) is an unsupervised learning technique that aims to maximize the variance of the data along the principal components.  The goal is to identify the directions (components) that capture the most variation in the data. In other words, it seeks to find the linear combination of features that captures as much variance as possible. The first component is the one that captures the maximum variance, the second component is orthogonal to the first and captures the remaining variance, and so on. PCA is a useful technique for dimensionality reduction when your data has linear relationships between features – that is, when you can express one feature as a function of the other(s). In such cases, you can use PCA to compress your data while retaining most of the information content by choosing just the right number of features (components).

Here’s an example to help illustrate how PCA works. We will use IRIS dataset. In the code below, the IRIS dataset is transformed into 2 components and scatter plot is created representing all the three classes such as Setosa, Versicolour and Virginica.

The above plot of data points after PCA was used for dimensionality reduction to 2 components shows a great separation between three different classes. Without the PCA, the plots such as below would represent the fact that classes ain’t separated clearly. This showcases the advantage of why PCA can be used for dimensionality reduction and a model trained with the transformed data will perform better than the original data.

Linear discriminant analysis (LDA) is another linear transformation technique that is used for dimensionality reduction. Unlike PCA, however, LDA is a supervised learning method, which means it takes class labels into account when finding directions of maximum variance. LDA aims to maximize the separation between different classes in the data. The goal is to identify the directions that capture the most separation between the classes. This makes LDA particularly well-suited for classification tasks where you want to maximize class separability. As with PCA, LDA assumes that your data is centered around the origin and that your features are uncorrelated with one another. You can center and decorrelate your data using scikit-learn’s StandardScaler and LinearDiscriminantAnalysis classes, respectively. Once your data has been cleaned and transformed, you can fit an LDA model to it using scikit-learn’s fit_transform() method. This will return a projected version of your data that has been reduced to the desired number of dimensions while maximizing class separability. In the code below, the IRIS dataset is transformed into 2 components and scatter plot is created representing all the three classes such as Setosa, Versicolour and Virginica.

As like PCA transformation, LDA transformation also results in clear separation of IRIS dataset classes which would not have been possible with scatter plot on original dataset.

Here are some key differences between PCA and LDA:

PCA is an unsupervised learning algorithm while LDA is a supervised learning algorithm. This means that PCA finds directions of maximum variance regardless of class labels while LDA finds directions of maximum class separability.

So now that you know how each method works, when should you use PCA vs LDA for dimensionality reduction? In general, you should use LDA when your goal is classification – that is, when you have labels for your data points and want to predict which label new points will have based on their feature values . On the other hand, if you don’t have labels for your data or if your goal is simply to find patterns in your data (not classification), then PCA will likely work better .

That said, there are some situations where LDA may outperform PCA even when you’re not doing classification . For example , imagine that your data has 100 features but only 10% of those features are actually informative (the rest are noise). If you run PCA on this dataset, it will identify all 100 components since its goal is simply to maximize variance . However , because only 10% of those components are actually informative, 90% of them will be useless. If you were to run LDA on this same dataset, it would only identify 10 components since its goal capturing class separability would be better served by discarding noisy features. Thus, if noise dominates your dataset then LDA may give better results even if your goal isn’t classification! Because LDA makes stronger assumptions about the structure of your data, it will often perform better than PCA when your dataset satisfies those assumptions but worse when it doesn’t.

So which technique should you use? That depends on what kind of data you’re working with and what your goals are. If you’re working with labeled data and your goal is to find a low-dimensional representation that maximizes class separability, then LDA is probably your best bet. However, if your goal is simply to find a low-dimensional representation that retains as much information as possible or if you’re working with unlabeled data, then PCA might be a better choice.

In general, it’s always worth trying both techniques on your dataset and seeing which one gives you the best results!

Disability insurance is the single best way that physicians can protect their future income if they become ill or injured and cannot work. And while there are hundreds of different policies out there that offer different benefits and terms, there is one thing that almost all have in common:

You’ll need to undergo a health screening before the insurance company starts the underwriting process.

But that is not the case with GSI.

GSI policies do not require a health screening or medical examination. That can certainly make it easier to get coverage, but that doesn’t necessarily mean that it’s better.

Not sure which type of policy is right for you?

Here’s what you need to know about GSI vs. Non-GSI policies and if your insurance policy should have a health screening.

GSI insurance is Guaranteed Standard Issue disability insurance. These policies supplement any traditional disability insurance you may already have through an employer-provided group long-term disability plan.

GSI plans are multi-life group plans that employers can offer to all (or none) of their employees. They do not require any health screening or any medical examination of any kind. Your insurance premiums and monthly benefits are not contingent on your health conditions.

Regardless of health, anyone can get approved. Even those with pre-existing conditions that other policy issuers might not cover.

Non-GSI insurance is not guaranteed. To get a non-GSI policy, you’ll need to have a medical evaluation. Additionally, your insurer will need to review the reports of your health screening, medical history, blood work results, and medical records.

Non-GSI insurance plans require you to jump through a few hoops to get coverage. However, they offer a variety of excellent benefits that GSI policies do not.

Non-GSI plans are flexible and customizable. You can choose your waiting periods, elimination periods, and select the true own-occupation definition of disability. You can add various optional riders to make your policy more comprehensive and protect yourself with more generous benefits.

Non-GSI plans are individual disability insurance policies that have nothing to do with your employer. And that means that you can take them with you from job to job without ever having to worry about a lapse in coverage or benefits.

GSI insurance can be a useful tool in protecting the future income of physicians who have pre-existing conditions and medical concerns.

Here are a few of the main reasons why Guaranteed Standard Issue disability insurance may be a good option for you:

As its name indicates, GSI insurance is GUARANTEED. With a short and easy application form, you can sign up for GSI insurance, skip the need for a health screening or medical exam, and get coverage quickly. As long as your employer offers it to you, you’re approved.

If the only disability insurance you have is that from an employer group plan, GSI can replace the income that the group plan doesn’t protect. Most employer group plans pay a limited benefit amount for a limited period of time, so that GSI can fill in that income gap. This allows you to receive more benefits per month than if you only had your employer’s group coverage.

For a physician or high-income earner that doesn’t already have an individual disability insurance policy and has a pre-existing condition, GSI is an excellent way to get yourself added protection if you become too ill or injured to work.

Similar: Why Relying on You Employer-Sponsored Disability Plan Can Hurt You

GSI insurance allows you to enjoy additional coverage if you have a poor medical history and can’t pass an insurer’s health screening. But the simple, easy, and guaranteed process comes at a price. Here are some of the drawbacks of having a GSI policy that you wouldn’t have if you had your own individual disability income insurance protection.

GSI insurance plans are convenient, easy to apply for, and guaranteed. But that doesn’t make them any less expensive. If you are healthy, it’s better to go out on the open market and compare quotes from various insurers that will underwrite an individual policy for you.

In the open market, you can usually get better rates, pay lower insurance premiums, and get more coverage and benefits. Getting disability insurance on the open market requires you to turn over your medical records, but you can get more and spend less on benefits than with a GSI policy.

It is entirely up to you what type of policy you want and which insurance company you want it from with an individual policy. With a GSI plan, your employer makes that decision.

It’s similar to the way your employer provides you with health insurance. Most employers choose one insurance provider and then give you the option to enroll in a healthcare plan with the one provider they’ve selected. GSI policies work the same way.

GSI policies have restrictions and limitations, including how much income they will replace if you become disabled. The amount of income replacement offered typically depends upon how many employees are enrolled in the plan.

In most cases, the more employees enrolled in the GSI plan, the higher the income replacement limit is.

With a GSI plan, each employee has the same type of coverage. Some GSI insurance providers, such as The Standard, offer varying coverage amounts based on age and occupation. Others have a standard coverage limit for all employees.

An individual insurance policy allows you to make changes throughout the life of the policy. These changes will affect your insurance premiums, but you usually can amend waiting periods, benefit periods, coverage amount, and drop or add riders as needed. GSI policies don’t offer nearly as much flexibility.

Some GSI plans allow you to make changes to waiting periods and benefits periods, but when that happens, those changes apply to all employees, not just you.

Some flexibility does exist within a GSI policy. But you usually aren’t the one who can make the call on changes. With an individual disability insurance policy, those decisions are yours and yours alone.

Depending on the type of GSI plan your employer chooses, you may or may not be able to add riders to your policy.

Why is this so important?

Because riders allow you to tailor your policy to your specific needs.

When you take out an individual insurance policy, you’re likely to be presented with a slew of optional riders that you can add to beef up the benefits of your coverage. Every rider you add increases your monthly premium, but the benefits they provide can be immeasurable.

Enrollment in a guaranteed standard issue program may preclude you from adding the riders you want to your policy.

The Cost of Living Adjustment rider increases your monthly benefits over time to account for inflation. This is especially important for young physicians just starting their careers.

The Future Increase Option affords you the option to increase your coverage amount as your salary increases. It also protects you from enduring future health screenings and going through the medical underwriting process again, should you want to increase your benefits later in life.

For young physicians, the Student Loan Repayment rider is a must. Should you become ill or injured before you’ve finished paying off your medical school debt, this rider will cover those loan payments for you. Without it, you’ll still have to pay off your student loans, even if you’re unable to work or earn income as a result of a disability.

Enrollment in a guaranteed standard issue program may prevent you from adding the riders you want to your policy. Before you sign up for a GSI policy, weigh your options to know exactly what benefits you can get and which ones you cannot.

THE most crucial element of any disability insurance policy is the definition of disability. How your policy defines disability determines what criteria you need to meet to collect benefits. No matter how much you pay in premiums, you won’t be able to collect one penny in benefits unless you meet that definition.

For physicians, the only acceptable definition of disability is true own-occupation. Under the true own-occupation definition, you will be eligible to collect benefits if your disability prevents you from doing some, part, or all of your job. Other definitions are far more restrictive and often don’t pay benefits unless you cannot work AT ALL.

Most insurance providers allow you to select the true own-occupation definition of disability as an additional rider. With disability insurance income through a GSI program, that rider and that definition may or may not be made available to you.

See also: Why Your Practice Needs a BOE Rider

Regardless of where you are in your career, it’s never too early to get long term disability insurance. And that’s because disabilities, illnesses, and injuries can strike at any time.

There’s no need to wait until you’re settled into your career to protect yourself with disability coverage. In fact, you can secure a policy when you’re still in residency or even as early as your last year of medical school.  The younger you are, the better.

Don’t think you need disability insurance when you’re young and healthy? You do. In fact, that’s the PERFECT time to choose a policy.

Here’s why:

Disability insurance is income protection. The younger you are, the more years you have to work. Which means that your future income earning potential is much greater than someone in their 40s or 50s. As a young physician, you have more future unearned income to protect, and disability coverage offers you a way to enjoy a percentage of that income, even if you become disabled early in your career.

Another reason to get disability insurance while still in residency or med school is that you’ll pay less in insurance premiums when you’re younger and healthier.

By adding a rider such as the FIO, you can opt for a smaller amount of coverage now, pay smaller premiums, and then increase your coverage to higher levels as you advance in your career. With a Non-GSI plan, you can do so without ever having to endure a health screening or an additional medical exam. See also: Nationwide Life Insurance Review

Unlike life insurance, which benefits your heirs when you die, disability insurance benefits both you and your family while you are still living. For this reason, every physician should have a long term disability insurance policy that protects their future income.

Afraid that you won’t be able to pass the health screening required for an individual disability insurance policy? For physicians with a pre-existing condition, such as cancer or diabetes, a GSI policy is a good option. And it may be your only one.

The bottom line is this:

Every physician needs disability insurance. Individual disability insurance is the best way to go. However, if your health prevents  you from passing a medical screening, a GSI policy is a viable alternative.

Similar: Your Full Guide to Tax Planning for Physicians

Not sure which policy is right for you? For more advice and guidance on selecting the best disability insurance policy, contact Physicians Thrive now.

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