Will the knowledge on types of errors like Type 1 error & Type 2 error be tested?
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. Achler uses a supervised learning approach to train the logistic regression model in predicting sentiment. Applying the receiver operating characteristics (ROC) technique and area under the curve (AUC) metrics Achler evaluates model performance on both the training and the ...
can some one explain the meaning of FEATURE ENGINEERING as i am getting confused between feature selection and feature engineering
node’s summation operator multiplies each (input) value by a weight and sums up the weighted values to form the total net input. The total net input is then passed to the activation function. pls explain this statement
Step 1 We apply ML techniques to a model including fundamental and technical variables (features) to predict next quarter’s return for each of the 100 stocks currently in our portfolio. Then, the 20 stocks with the lowest estimated return are ...
Exhibit 2 Results of the Dickey-Fuller Tests Time Series Value of the Test Statistic Standard Error t-Statistic Significance of t Defective assemblies per hour 0.0036 0.0023 1.591 0.1123 Outside air temperature –0.423 0.0724 –5.846 0 Assembly line speed –0.586 0.043 –13.510 0 Dennehy tells Malloy about the Dickey-Fuller test results, stating: “We can safely use regression to ...
Logistic Regression 1 Logistic Regression Results Model: Logit Pseudo-R2: 0.057 Dependent Variable: label No. Observations: 1594 Log-Likelihood: -451.66 Df Model: 7 LL-Null: -478.86 Df Residuals: 1586 LLR P-Value: 1.97E−9 No. Iterations: 10 Coefficient Std.Err. z-Statistic P-Value > |z| [0.025 0.975] Intercept -2.0350 0.5221 -3.8979 0.0001 -3.0583 -1.0118 net_assets -0.7667 1.3571 -0.5649 0.5721 -3.4265 1.8932 portfolio_stocks -0.0089 0.0051 -1.7550 0.0793 -0.0188 0.0010 portfolio_bonds -0.1113 0.0729 -1.5263 0.1269 -0.2543 0.0316 price_earnings 0.0292 0.0200 1.4647 0.1430 -0.0099 0.0683 price_book -0.0390 0.1029 -0.3791 0.7046 -0.2407 0.1627 price_sales 0.3432 0.0777 4.4160 0.0000 0.1909 0.4956 price_cashflow -0.0502 0.0363 -1.3805 0.1674 -0.1214 0.0211 Determine for Logistic Regression 1 which of the following is closest to the change in the probability that an ETF will be a ...
Analysis of Monthly Seasonality of Excess Portfolio Returns Regression Statistics Multiple R 0.321 R-Squared 0.103 Adjusted R-Squared -0.014 Standard Error 8.100 Observations 96.000 ANOVA df SS MS F Signif. F Regression 11 634.679 57.698 0.879 0.563 Residual 84 5511.369 65.612 Total 95 6146.048 Coeff. Std. Error t-Stat. P-Value Intercept 1.263 2.864 0.441 0.660 Jan 1.311 4.050 0.324 0.747 Feb -3.756 4.050 -0.927 0.356 Mar 3.495 4.050 0.863 0.391 Apr 0.174 4.050 0.043 0.966 May 0.714 4.050 0.176 0.861 Jun 0.944 4.050 0.233 0.816 Jul -0.571 4.050 -0.141 0.888 Aug -0.445 4.050 -0.110 0.913 Sep -1.744 4.050 -0.431 0.668 Oct 4.261 4.050 1.052 0.296 Nov -5.311 4.050 -1.311 0.193 Question Determine using Exhibit 2 which one of the following statements is most likely to be correct. ...
You are a junior analyst at an asset management firm. Your supervisor asks you to analyze the return drivers for one of the firm’s portfolios. She asks you to construct a regression model of the portfolio’s ...
Isprobabilistic approachrs: scenario analysis decision trees and simulation part of our level2 exam? I was going across my review lectures and this portion was there… I have my exam in nov 2023. Should I watch this lecture or leave it? ...