A Kareem
• Member for 1 year, 9 months
• Last seen more than a week ago

That is generally not true, to be more accurate we can say that L1 promotes sparsity. if a weight is larger than 1 then L2 cares more about it than L1 while if a weight is less than 1 then L1 cares ...

One major difference is that the F1-score does not care at all about how many negative examples you classified or how many negative examples are in the dataset at all; instead, the balanced accuracy ...

It really depends on how much data (samples) you have instead of how many features each samples has. And more importantly it depends on how you plan to structure the problem into an Environment with ...

Reading the article they state that The two vertical lines represent the $L^2$ norm of the error, or what is known as the sum-of-squares error (SSE) Otherwise known as the Euclidean norm, ...

While I don't have a module named graphviz I can still try to help. Reading the documentation for GridSearchCV, I can see that there's a attribute called best_estimator_ that provides the estimator ...

I quickly checked your calculation and you seem to have miscalculated the gini(annual income) gini(annual income)=1-((5/20)^2+(12/20)^2+(3/20)^2) = 0.445 When it actually equals 0.555 (you ...

I suggest first separating the month column into day and month using str.split('-') # create test data df = pd.DataFrame(['20-Apr', '19-Mar', '4-Dec'], columns=['month']) # create day column df['day']...

The main reason is that a linear combination of the input followed by a non-linearity stacked on top of eachother is a universal function approximator. Which means that no matter how complicated the ...

You can read the file line by line, discard blank lines, and wrap the lines that are left by a list to get the same as your desired result with open(file_name, 'r') as f: print([[x.strip()] for x ...

Your problem isn't just a low recall value, your problem is your model needs improving. A high accuracy with a highly unbalanced dataset means practically nothing since simply predicting the most ...

Theoretically, a model should be big enough to have a low bias (avoid underfitting), but not too big as to have too high of a variance (avoid overfitting), called the bias-variance tradeoff Whether ...

EDIT: It seems I misunderstood the task at first, so here's my correction. Hope it works this time It seems like what you're trying to do is similar to what is in the documentation under examples/...

Have you tried using the pandas.read_json method? (documentation) And it looks like your json is structured like 'records' so use pd.read_json(_, orient='records')

You seem to be describing the method cut in pandas (documentation) This method does exactly what you want, if you want to separate a dataframe into n equal-sized bins or manually specify the ranges. ...

According to the documentation, the function RandomizedSearchCV accepts a scoring string that can take any value from this table and you can even implement your own custom scorer depending on what ...

Well yes it would be slow because you are opening and closing the file for every iteration of the for loop. A general rule in programming is that if the file is not constantly changing, then only open ...

Good observation, and yes, they are in fact equivalent ways of computing the entropy of a bernoulli random variable. To begin, you have to notice that in the openai code, we do not have the value of ...

In the second line of the for loop create_grid.fit(X_train.fillna(X_train.mean(), y_train.fillna(y_train.mean())) You need an extra closing parenthesis like so create_grid.fit(X_train.fillna(...

What you're explaining is basically almost every CNN model where you basically have a fully connected layer at the end of the convolutions and that is equivalent to having a linear/logistic regression ...

Building a decision tree is a process where the algorithm picks the first feature to split on $i_1$ from the set of features $n$ that it can split on $i_1={1,...,n}$. After splitting, the algorithm ...

That answer comes from the set of weights $w$ (or $\theta$) that analytically solves the cost function which is defined to be $J(\theta) = (X\theta - y)^T (X\theta - y)$ (See here for more info) ...

I would first decide to bin the x-axis such that it can be plotted in groups. Thus if we want to for example group them into bins of width 5 then plot them next to each other we would do something ...

In Python, to generate random numbers from a certain distribution you would pick the corresponding distribution from np.random (documentation) and pass the corresponding parameters. Thus to draw from ...

I think the cause of the error is the np.math.factorial(k) function call, since curve_fit passes a numpy array as the first parameter to the poisson function, and if you try to run the code np.math....

I'm assuming you want to create a point that, each column by itself appears normal, but when looking at all the columns appears as if it's an outlier (thus you'd need some sort of outlier detection). ...

First, If you calculate the mean along dim=1 the output shape should be [a, c]. If you want to mask the mean that's less then a threshold and set it to zero you can do # generate data torch....